Central Bank of Swaziland (CBS) Research Paper Commissioned by the Common Market for Eastern

and Southern Africa (COMESA).

Effects of Fiscal Policy on the Conduct and Transmission of Monetary Policy in Swaziland.

Policy Research and Macroeconomic Analysis Division of the Central Bank of Swaziland Policy Research and Statistics Department1

September 2015.

Abstract

The estimated VARs Johansen Cointegration approach are found to be stable and the Wald tests rejects all short run impacts of the fiscal balance on inflation and discount rate differential between Swaziland and South Africa. The fiscal balance has a long run significant impact on the inflation rate where a rise in the fiscal balance by a percentage point leads to a fall in inflation in the long run by 0.13 percent. The Fiscal Balance improvement leading to a fall in inflation widens the interest rate differential between South Africa and Swaziland lowering domestic interest rates in the process significantly in the long run. Fiscal shocks emanate from external factors with South Africa inflation having strong causality effects on the fiscal balance in the short run with a chi-sq. of 16.37 and a low p-value of 0.0003 in the Wald short run causality test. The fiscal balance is not financed through money creation in the short run where money creation has a chi sq. of 3.20 and p value of 0.20 as shown in the Wald tests but fiscal shocks would still lead to persistently high inflation and interest rates only to stabilise in the tenth year. The financial crisis and introduction of value added tax are found, tough insignificant, to reduce and increase inflation in the short run by 0.19 and 0.36 percent respectively and have no long run impact. The variance decomposition of inflation is mainly driven by the discount rate and fiscal balance in the long run.

This Research Paper should not be reported as representing the views of the CBS. The views expressed in this Research Paper are those of the author(s) and do not necessarily represent those of the CBS or CBS policy.

1 Prepared by: Simiso F. Mkhonta. Authorised for distribution by:

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Contents Page

1. Introduction……………………………………………………………………………………………………………..4(a) Inflation and the Fiscal Deficit in 1980-2000………………………………………………………4(b) Inflation and the Fiscal Deficit in 2000-2015………………………………………………………5

2. Review on Fiscal Performance………………………………………………………………………………….63. Review on Existing Legal and Institutional Developments Discouraging and Promoting Fiscal

Dominance and Effective Coordination of Monetary and Fiscal Policy……………………..84. Challenges of Existing Fiscal Policy……………………………………………………………………………95. Key Features of the Operational Framework for Fiscal Policy, Monetary Policy and

Interaction between Fiscal and Monetary Policy……………………………………………………..116. Literature Survey……………………………………………………………………………………………………16

6.1 Theoretical and Empirical Literature on the Different Channels through which Fiscal Policy can affect Monetary Policy………………………………………………………………………16

6.2 The Impact of Fiscal Policy on Monetary Policy………………………………………………….166.3 Fiscal Policy Impact on Monetary Policy under Fixed Exchange Rate………………….16

6.4 Economic Mechanical Operation of Fixed Exchange Rate Regime…………..247. Methodology and Data……………………………………………………………………………………………..268. Econometric Results………………………………………………………………………………………………….27

8.1 Units Root Tests………………………………………………………………………………………………….278.2 The Long Run Inflation Estimation Equation and Diagnostic Tests……………………….298.3 The short Run Inflation Estimation Equation and Diagnostic Tests………………………388.4 The Long Run Discount Rate Differential Equation Estimation and Diagnostic

Tests…………………………………………………………………………………………………………….408.5 The Short Run Equation on Discount Rate Differential……………………………………….428.6 The Short Run Value Added Tax and Financial Crisis on Inflation Estimation

Equation……………………………………………………………………………………………………..469. Summary and Policy Implications…………………………………………………………………………….48

References

Figures

1. The Timeline for Growth and Fiscal Performance……………………………………………………62. Government Salaries and Capital Expenditure and Revenues………………………………….73. AR Root Stability Tests Equation 8(i)………………………………………………………………………334. Excess Liquidity………………………………………………………………………………………………………34 5. Impulse Response Function Equation 8(i)……………………………………………………………….356. Crude Estimation of the Trend for Velocity of Money…………………………………………….377. AR Root Stability Test Discount Differential Equation……………………………………………..428. Impulse Response Function; Discount rate Differential Equation……………………………449. Value Added Tax and Inflation………………………………………………………………………………..48

Tables

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1. ADF Statistics for Testing for Unit Root……………………………………………………………….28.

2. Lag length Criterion………………………………………………………………………………………………29

3. Johansen Unrestricted Cointegration Rank Test (Trace)…………………………………………30

4. Johansen Unrestricted Cointegration Rank Test (Maximum Eigenvalue)………………..31

5. Diagnostic Tests for Inflation Equation…………………………………………………………………..32

6. Wald Causality Test……………………………………………………………………………………………397. Diagnostic Test for Discount Rate Differential Equation……………………………………..418. Discount Rate Differential Equation Stability Test……………………………………………….429. Wald Causality Test Discount Rate Differential Equation…………………………………….4510. Diagnostic Results Value Added Tax and Financial Crisis Equation……………………….47

Appendix I Definitions and Sources of Variables……………………………………………………………..50

Appendix II Tables and Figures……………………………………………………………………………………....50

Table A111 Cointegrating Equation –Johansen Test; Longrun equation –for Inflation. VAR

Estimation and Diagnostic Tests…………………………………………………………………………………….50

Table A112 Cointegrating Results of Long Run Differential Equation …………………………….55

Table A113 Wald Short Run Causality Test for Discount Rate Differential …………………….57

Table A11 4 Block/Joint VAR Test for Causality…………………………………………………………….59

Table A115 VAR Heteroskedasticity Test Results …………………………………………………………60

Table A116 Short Run Inflation Equation (VECM)…………………………………………………………61

Table A117 Final VAR Stability Test for Inflation Equation……………………………………………63

Table A118 Deficit Testing for Short-run Effects …………………………………………………………66

Table A119 Exogeniety Tests ………………………………………………………………………………………68

References………………………………………………………………………………………………………………….73

1. INTRODUCTION

3

Swaziland has not experienced a deficit of greater than 10 percent, the highest being 9.5

percent experienced in fiscal year 2010/11 during the height of the financial crisis, since the

1980s. Fortunately the monetary authorities have not been apt to finance the deficit

through money creation. Swaziland has seen prudent monetary policy owing to the

membership of the country to the Common Monetary Area (CMA). Botswana, Lesotho and

Swaziland due to their close proximity and trade links with South Africa formed the Rand

Monetary Area (RMA) in 1974 together with South Africa and later Botswana opted out of

the monetary union in 1975. The membership to the CMA requires Swaziland to maintain

the pegged exchange rate with the Rand which would require Swaziland to exercise prudent

fiscal policy. The tendency of fiscal deficits to increase money supply and put pressure on

the exchange rate encourage the fiscal authorities to limit the deficits they run both in

magnitude and frequency. The Government of Swaziland though did not live up to

expectations when a deficit of 9.5 percent was run coupled with minimal reforms on the

expenditure side during the height of the crisis. The Government instilled the impression

that should an environment of low revenue persist then debt would rise to a level where

there is a credit crunch and default leading to a serious contamination of the economy to a

wider scale. The credit that was extended to Government in 2012 as a measure to

ameliorate the effects of the global financial crisis was quickly reversed in 6 months to

maintain an environment conducive for the existence of the CMA and financial stability but

persistence of low revenues would see government defaulting until there are serious

measures to curb expenditure in particular the huge wage bill.

(a) Inflation and the Fiscal Deficit in 1980-2000

Swaziland experienced a cyclone domoina and a severe drought in the 1980s resulting in

government running deficits which apparently were inflationary given that in the 1980 the

highest inflation figures of 20 percent were recorded though the deficit was not finance

through money creation. The high inflation locally could also be traced from inflation

development in Swaziland’s major trading partner South Africa. P. Burger and M. Marinkov

(2001) found that the periods of high inflation (1980-89 and 1990-2000) were periods

before the introduction of inflation targeting where inflation was implicitly targeted and

there was less success in containing inflation. Wolassa L. Kumo (2015) also concluded that

by adopting the inflation targeting monetary policy framework since 2000, South Africa

4

succeeded in achieving low and stable general price level after a pre-inflation targeting

regime period covering 1960Q1-1998Q4 where South Africa adopted various monetary

policy frameworks including exchange-rate targeting, discretionary monetary policy,

monetary-aggregate targeting and an eclectic approach resulting in high and more volatile

inflation which was imported into Swaziland. The high inflation rate in Swaziland in the

1980s were therefore not due to fiscal deficits but was mainly imported from South Africa

which was still finding its footing in monetary policy.

(b) Inflation and the Fiscal deficit 2000-2015

The inflation trajectory moderated post 2000 after South Africa implemented inflation

targeting in 1999, due to the great moderation. In the early 2000s the Government ran fiscal

deficits after the Dotcom Stock bubble of 2000 which resulted in suppressed world demand.

SACU receipts fell from a growth of 14 percent in 2000 to growths of 8 and 7 percent in

2001 and 2002 before rebounding back to 14 percent in 2003 not enough to immediately

restore previous levels because of the low base. Even with the recovery deficits were run as

Government implemented the Salary Review Exercise.

Monetary policy in Swaziland is therefore defecto inflation targeting due to the pegged

exchange rate monetary policy regime which helps instil fiscal restraint. This was further

demonstrated during the height of the global financial crisis where Swaziland experienced a

negative shock on SACU receipts putting the fiscus under pressure which led the Central

Bank to lend money to the Government to the tune of E680 million under the CBS order but

was prudent enough to repay the money in 6 months from the beginning of the fiscal year

following the fiscal year in which the debt was procured. The weakness of fiscal policy in

disarraying monetary policy therefore lies in the will of the fiscal authorities to reign in

expenditure during long periods of low Government revenue in particular SACU which

would otherwise spill over to the monetary sector in terms of rampant increases in money

supply and debt.

2. Review on Fiscal Performance.

5

Swaziland’s fiscal position is mainly supported by the South African Customs Union (SACU)

receipts which have average 50 percent of total government revenue since 1980. SACU

receipts fell to below 40 percent of total government revenue in the late 1980 and early

1990s and the fiscus slipping to deficits as seen below in figure 1. The competition for the

local economy intensified when firstly Namibia was liberated in 1990; followed by cessation

of hostility in Mozambique in 1992 and finally a democratic settlement in South Africa was

obtained in 1994. This resulted in the government running a deficit which was financed

through Treasury bill and bonds. Though the stabilization of South Africa in 1990s resulting

in growth rates of above 5 percent on average resulted in a stabilization of SACU receipts,

there was a slump in SACU receipts, in the same token as growth in South Africa faltered to

record a recession during the global financial crisis in 2010.

Figure 1. The Time Line for Growth and Fiscal Performance.

198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013

-50

0

50

100

150

200

-20

-10

0

10

20

30

40

50

SACU % Change The Fiscal Deficit as % of GDPGDP growth Change in Government Expenditure

SACU

% C

hang

e

Global Fin-ancial Crisis-low growth-low SACU growth

Asian Fin-ancial Crisis & Dot Com bubble-low growth-low SACU growth

SACU wind-fall -high oil pricesrecovered growth

high growth -low SACU growth (low base) -defcit

Boom Period

SACU recovers

Source: Central bank of Swaziland

The high economic growth rates recorded in the 80s resulted in government running a

surplus of 5.9 percent. Government expenditure increased in the 1990s as recurrent

expenditure increased buoyed by the good GDP growth emerging from the 1980s. The high

inflation rate of 20.3 percent in 1987 and 14.1 percent in 1990 contributed to the high

increases in Government expenditure. Government expenditure again increased

6

substantially in the year 2000 where the expenditure was mostly driven by millennium

projects capital expenditure. Government expenditure saw another substantial in

expenditure in 2004 when the salary review exercise was implemented. The SACU receipts

picked up reaching a peak of close to 70 percent of total government revenue in the years

2006, 2007 and 2008, before sliding, starting from 2009, down to a low of 38 percent of

Government revenue in 2010 due to the global financial crisis. As a consequence the

government ran deficits as high a deficit as 9.5 percent in 2010 financing the deficit through

treasury bills and, bonds and through money creation which was quickly reversed in 6

months to shield the monetary regime from collapsing. Swaziland Government expenditure

in general is driven by high revenues and high inflation which needs to be compensated to

keep Government operations stable. Government has no appetite to set aside funds in

periods of high government revenue because of a level of employment that has officially

been above 20 percent.

Figure 2 Government Salaries and Capital Expenditure and Revenues

19801983

19861989

19921995

19982001

20042007

20102013

0

5

10

15

20

25

30

35

40

45

Government Salaries and Wages percent of GDP as % of GDPCapital Expenditure percent of GDP as % of GDPGovernment Revenue and Grants as % of GDPGovernment Expenditure as % of GDP

perc

ent

Source: Central bank of Swaziland Quarterly Reports

This points to the fact that the government and the central bank do coordinate to shield the

monetary regime as stated by the Central Bank Order of 1994. The limit on Central bank

borrowing displays that there is an implicit limit put on fiscal policy by monetary authorities

Sargent and Wallace (1981)thus Swaziland can be viewed as a monetary dominant

monetary-fiscal policy set up even though the limits are on the high side In figure 2

government expenditure has steadily increased with increases in revenue. When

government revenue fell in 2010 and 2011 due to the financial crisis expenditure did not

7

adjust commensurate to a fall in revenue resulting in a huge deficit. The expenditure

remained high due to statutory commitment and resistance by the authorities in

implementing fiscal reforms of cutting recurrent expenditure but instead the capital

programme was cut compromising long term growth. The SACU receipts shock result in a

huge deficit as they account for a significant part of the revenue and coupled with a sticky

down ward wage bill.

In a nutshell the economy ran deficits in the early 80s after independence to include more

of the population in the Government budget. The was a short cut in economic growth as

the country experienced floods in 1984 but the increased expenditure after independence

and the location of firms in the economy as it was then a comparatively conducive

environment for investment in a politically turbulent Southern African region saw the GDP

growth rates rising to reach above 10 percent in 1990. The Asian financial crisis and the

Dotcom bubble in the late 90s and early 2000 saw the government being driven to a high

deficit as revenues fell. The oil price boom saw an upturn in SACU in 2005 only to experience

the global financial crisis in 2009, 2010 and 2011 where SACU receipts fell and recovered to

normal levels in 2012.

3. Review on Existing Legal and Institutional Developments Discouraging and Promoting Fiscal Dominance and Effective

Coordination of Monetary and Fiscal Policies.

The Government of Swaziland in the quest to promote monetary and fiscal coordination

with the interest of not impinging negatively on monetary policy in terms of excessive

credit promulgated Act No. 7 of 1994 providing for the issue of Treasury Bills and

Government Stock setting a limit of SZL/ZAR300 million. The act was amended in 2010 at the

height of the global financial crisis to a limit of 25 percent of GDP, which still is a limit

commensurate to the state of the economy, as a means to instil fiscal discipline though the

limit could be considered on the high side. Sargent and Wallace (1981) note along the lines

of the development of the limit that the demand for bonds place an upper limit on the stock

of bonds relative to the size of the economy and the creation of money on the other hand is

determined by who sets the limits between the monetary and fiscal authorities. Does the

monetary authority set by how much base money should grow or the fiscal authorities set it

8

by running any level of deficit with no ceiling on base money growth. Once the limit has

been reached the principal and interest due on the bonds already sold to mob up liquidity

and fight inflation must be finance at least in part by seignorage, requiring the creation of

additional base money which would ultimately defeat the initial disinflationary intentions of

setting the limit. Raising interest rates by the Central bank is no option under the fixed

exchange rate regime more in particular because of the resultant effects it would have on

growth. Thus setting the limits on government domestic credit by legislation would not need

the Central bank to sell bonds to fight inflation by mopping up liquidity. The Central bank

avoids a situation where it will have no option but to create more money to pay off the

principal interest on the bonds and treasury bills. The monetary authorities would have the

option of rolling over the debt till there is enough growth to absorb the excess liquidity

resultant from financing the principal interest rates on bonds through seignorage. Sargent

and Wallace’s model does not look at the options of rolling over debt as growth emerges

because they constructed a monetarist economy model.

The Central Bank of Swaziland Order of 1974 addresses the limits to money creation as

means to finance government deficits. The order states that the extension of credit to

government by the central bank shall at no time exceed twenty percent of the average

annual revenue of Government. The Order further states that from time to time

Government in respect of temporary deficiencies of current budget revenue, subject to

repayment within six months following the end of the financial year of the Bank in which

they are granted can borrow from the Central Bank. When calculating 20 percent of

Government revenue in 2013 the limit runs up to slightly above E1.8 billion which is above

the board, more in particular because Swaziland is in a fixed exchange rate regime. Krugman

(1979) observe that when government is no longer able to defend a fixed parity because of

the constraint on its actions, there will be a “crisis” in the balance of payments. Though

there is a constraint set by the order on Government seignorage financing it is deemed to be

on the high side because an increase of E1.8 billion in base money for the year 2013 for

example would translate into an increase of over 250 percent, which would obviously not

auger well for monetary policy and the economy at large.

The Governor in consultation with management and the monetary policy coordination

committee decides on interest rates. The monetary policy coordination committee is chaired

9

by the governor with part of bank management and stakeholders’ representatives from

outside the bank as members..

4. Challenges of Existing Fiscal Policy

The most pressing challenge for fiscal policy in Swaziland is the fact that on average with

data from 1980 to 2013 revenue from SACU has been above 50 percent of total revenue.

The fears were confirmed in 2010 during the height of the global financial crisis where

Swaziland experienced a negative shock in SACU receipts to the tune of 49 percent resulting

in a deficit of close to 10 percent. The government has been working on diversifying the

sources of revenue and has also restructured the collection of taxes by forming the

Swaziland Revenue Authority (SRA) and introducing value added tax in 2012.

The highest wage bills in sub-Sahara Africa at 15 percent of GDP on average from 2006-2010

and rising to 17.5 percent of GDP in 2014’is Swaziland’s other fiscal challenge. Olivier

Basdevant (2012) observed that the risk of a significant loss of SACU revenue calls for fiscal

reforms. Wage bills have become a frequent element of conditionality in IMF programs

with 17 out of 42 Poverty Reduction and Growth Facility (PRGF) programs (13 of 24 PRGF

programs in Africa) from 2003-2005 alluding to some form of wage bill ceiling. Basdevant

further notes that Botswana, Lesotho, Namibia and Swaziland shock on SACU receipts may

emanate from at least three structural factors: (i) a slowdown in global economic activity,

which would affect the SACU revenue pool; (ii) a reduction in the common external tariff

rates as a result of trade liberalization; (iii) the creation of the South African Development

Community (SADC) customs union and the fourth not mentioned in the paper would be the

loss of fiscal autonomy as SACU creates a development fund. Basdevant probably does not

mention this because it can swing either way depending on how each countries campaigns

for a share in the development fund which would be established by SACU.

The underlying bloated recurrent and non-discretionary expenditure, notably on the wage

bill, do not auger well in the event of a government revenue shock, which would highly likely

emanate from SACU receipts. There is therefore an urgent challenge to lower the wage bill

so that the budget can absorb shocks so as to avoid dire economic consequences coupled

with revenue diversification.

10

5. Key Features of the Operational Framework for Fiscal policy,

Monetary Policy and interaction between Fiscal and Monetary

Policy.

Paul Hilbers (2005) observes that the most important objective of central bankers is price

stability, but he further notes that there are others like economic development and growth,

exchange rate stability and safeguarding the balance of payments, and maintaining financial

stability. He notes that key variables in monetary policy include interest rates, money, credit

supply, and the exchange rate. Monetary and fiscal policies implementation by

independent of each other monetary authorities and fiscal authorities respectively is in itself

far from being independent. The implementation of one influences the other. It is therefore

essential that a consistent monetary-fiscal policy mix be pursued.

Hilbers in his 2005 paper presented at the IMF Seminar on Current Developments in

Monetary and Financial Law highlights both direct and indirect channels through which

fiscal policy affects monetary policy. Figure 1below aids the expression of both indirect and

direct channels of fiscal policy in affecting monetary policy.

(a) Excessive fiscal deficits may tempt government to finance the deficit by printing

thus leading to expansionary monetary policy which fuels inflationary pressures

and leads to a real depreciation of the currency. Depreciation of the currency

may lead to balance of payments crises.

(b) Even when deficits are not financed by money creation there is the concern of

the private sector being crowded out of the credit market by government

through high interest rates and outright non availability of credit funds for the

private sector, compromising economic growth and development in the process.

(c) Too much dependence on foreign funding of the deficit could result in exchange

rate and/or balance of payments crisis which would be worrying for the Central

bank as it also has direct implications for the maintenance of a healthy level of

reserves.

(d) A more direct way fiscal policy can affect Central bankers is by raising revenue

through increases or imposition of indirect taxes where a once off increase can

11

lead to a wage-spiral, depending on labour market forces, leading to

permanently high inflation and inflationary expectations.

(e) Confidence in the economy is compromised by large and persistent deficits which

may lead to the collapse of the monetary regime as noted by Makoto Richard

and Ndedzu Desmond (2012) in the case of Zimbabwe.

(f) Forward looking agents raise savings and reduce consumption in the light of

perceived unsustainably fiscal deficits leading to contractions in the economy

and ineffectiveness of expansionary monetary policy in resuscitating the

economy. This could be viewed as a fiscally induced liquidity trap which can be

reversed by changes in the nature of government expenditure.Fiscal

restructuring in general could help restore confidence in the economy and

resuscitate private spending.

(g) Expansionary fiscal policy will affect the Central bank whether the Central Bank is

independent or not. As expansionary fiscal policy may lead to inflation and as a

result Central banks hike interest rates straining the economy, attracting hot

money and increasing currency risks. Sterilization becomes costly for the Central

bank leading to inflationary pressures and at a later stage the reversal of the hot

money due to external factors. This may ultimately lead to a depreciation of the

currency inviting more inflationary pressures. Turkey in 1994 and 2001 and

Mexico in 1994 are relevant cases.

(h) The desire to develop financial markets with the aim of achieving economic

growth and development, funding deficits and debts and proper management of

liquidity with more flexible interest rates also results in the interaction of

monetary and fiscal policies.

The adoption of fiscal rules play an important role in avoiding large and persistent deficits

which may affect monetary policy, through variables like inflation, interest rates and

balance of payments. Transparency in monetary and fiscal policies is also vital in ensuring

that monetary and fiscal policies are coordinated effectively as promoted by the

International Monetary Fund (IMF). Most importantly, the stance of the fiscal and monetary

policy mix and the magnitude of the effect of fiscal policy on monetary policy are

determined by the nature of dominance between fiscal and monetary policy. A

12

fiscal/monetary dominant set up as espoused by Thomas J. Sargent and Neil Wallace (2012)

is as follows;

(a) where a fiscal dominant scenario exists the monetary authorities face the constraints

imposed by demand for government bonds and there is a great tendency for fiscal

authorities to run deficits that monetary authorities will be unable to control either

through the growth rate of base money or inflation forever. As put by Sargent and

Wallace (2012) the monetary authority’s inability to control inflation permanently

under these circumstances follows from the arithmetic’s of constraints it faces

emanating from how the deficit is financed.

(b) where a monetary dominant scenario exist , the fiscal authorities then face the

constraints imposed by demand for bonds, since it must set its budget so that any

deficits can be financed by a combination of seignorage chosen by the monetary

authority and bond sales to the public. Under this scenario monetary authorities can

permanently control inflation.

Therefore the severity of the effects that the fiscal deficit could have on monetary policy is

basically determined by whether a fiscally or monetary dominant scenario is obtained.

Under a fiscally dominant scenario the effect on monetary policy are detrimental to the

economy and pronounced, Makoto Richard and Ndedzu Desmond (2012) and Jean-Claude

Nachega (2005) found this to hold for Zimbabwe and the Democratic Republic of the Congo

(DRC) respectively.

The framework therefore outlines the different possible conduits through which fiscal policy

can affect monetary policy. The possibilities it should be stated are not exhaustive but are

those that have been cited in literature. They are not rigid but vary from economy to

economy depending on the disposition of monetary and fiscal policy and the underlying

economic and political dynamics. Ndezu and Makoto (2012) and Jean-Claude Nachega

(2005) found that the deficits and their monetisation were brought about by political

dynamics.

13

Figure 2 Organogram Frame work for Fiscal and Monetary Policy Interaction

14

Fisc

al D

efici

t

finance through domestic credit

market

high interest rates lead to;- crowinding out of the private

sector and weak economic growth-attract hot money and risks of

reversal of hot mney and balance of payments crisis. The prevailing

high interest rates though with more capital into the ecomnony still exclude the private sector

-ricadian equivalence

development of financial markets

proper management of

liquidity

economic growth

financed through monetisation

-inflationary pressures-balance payments

crsis/currency crisisweak economic growth

ricadian equivalence

indirect tax impostion wage-spiral and permanetly high inflation

financed through international credit market

balance of payments crisis/currency crisisricadian equivalence

Transparency

Fiscal Rules

Fiscal/Monetary Dominance

6. LITERATURE SURVEY

6.1 Theoretical and Empirical Literature on the different

Channels through which Fiscal Policy can affect Monetary

Policy.

6.2.1 The Impact of Fiscal Policy on Monetary Policy.

The relationship between budget deficits and inflation has been investigated extensively for

both industrial and developing countries with mixed results. The debate on the desirability

of deficits came to the fore front and developed primarily into two camps during the epoch

of the Great Depression with eminent scholars like Keynes stirring the debate. Deficits got

extensive attention during the period between the Great Depression in the 1930s and post-

World War II in the 1950s. John Maynard Keynes (1936) popularised the need for

governments to run deficits during recessions to compensate for the shortfall in aggregate

demand but should run surpluses in boom times so that there is no net deficit over an

economic cycle. Keynesian approach is therefore not likely to be inflationary owing to the

fiscal discipline entrenched in Keynesian thought though Keynes appreciates the advantages

of monetization at least to a certain limit. Fiscal discipline is defined as the capacity of

government to maintain smooth financial operation and long-term fiscal health; it branches

into (1) multiyear perspective on budgeting and (2) mechanism to maintain fiscal health and

stability over business cycles Yilin Hou (2003). An inflationary deficit cannot be seen to bring

fiscal discipline as resultant inflation would lead to higher real fiscal deficits according to

Aghevil and Khan (1977). The neo-classicalist on the other hand, the Chicago school of

economic and Australian school of economics among others believe deficits are a bad thing

in that they are inflationary. They argue that this is because governments pay off debts by

printing money, increasing the money supply and creating inflation. Jean-Claude Nachega

(2005) found that the degree of institutional structure linking budget deficits to money

creation has changed over time in that in the period from 1965 to the mid-1970s the DRC

was characterised by relative political stability, resulting in lower monetization of the deficit

15

and lower inflation. Nachega’s conclusion point to the fact that the nature of deficit

financing determines the impact of the budget deficit on inflation where monetization of

the budget deficit was found to be inflationary in the DRC compared to periods where the

budget deficit was less monetized. From Nachega’s conclusion the Fiscal Dominance

hypothesis which is central in the study of the impacts of budget deficits on monetary policy

can be appreciated.

It has often been argued that high inflation in developing countries is a result of persistently

high monetised deficit. The FD hypothesis that high inflation is a result of fiscally dominant

government with large and persistent deficits financed through money creation is found to

hold for developing countries. Apheous Ncube, Jackie Kitiibwa and Jean-Baptiste

Havugimana (2013) cite fiscal policy as one of the impediments in implementing monetary

policy in developing countries. It has also been observed in developing countries that

nonfiscal real disturbances or high inflation may lead to lower real tax revenues hence

higher real deficits rendering the deficit and money supply endogenous to the inflationary

process Nachega (2005).

Early studies confirm the FD hypothesis since Agheveli and Khan (1977). Agheveli and Khan

first showed that the growth in the money supply and inflation are linked in a two-way

relationship in Brazil, Colombia, the Dominican Republic and Thailand over the period 1961-

74. They ultimately found out that fiscal deficits play an important role in the inflationary

process, and that increases in these deficits are largely owing to the differences in lags of

government expenditures and revenues.

The extent to which government deficits/government debt affect, interest rates, money

supply, inflation and the balance of payments hence reserves and the exchange rate has

been studied by authors like Jean-Claude Nachega(2005), Ieva Sakalauskaite (2010), Michael

Kumhof ,and Douglas Laxton(2009) and lately Olivier Balanchard (2004) though in different

approaches and contexts. They invariable found that fiscal dominance or rather persistent

and high deficits led to a change in the conduct of monetary policy due to its effects on

interest rates, when interest rates respond to government debt Michael Kumhof , Ricardo

Nunes and Irina Yakadina (2008) and when interest rates make government debt more

attractive Olivier Balanchard (2004). Further studies looked at money supply and inflation

16

including Jean-Claude Nachega (2005), and money supply(monetary policy) on exchange

rate in Ieva Sakalauskaite (2010) and inflation under three central bank monetary

accommodation scenarios; excess, net and statutory central bank credit to government in

Makoto Richard and Ndedzu Desmond (2012).

Jean-Claude Nachega (2005) in his study on the Democratic Republic of the Congo (DRC)

argues that an increase in the budget deficit leads to increased seigniorage and the money

creation begets inflation. He found out that the degree of the institutional structures linking

budget deficits to money creation changed over time. The DRC experienced different epochs

defined by political changes that had varying effects on money creation. During period of

high political instability public finances put pressure on monetary policy leading to

monetization of the deficits hence higher inflation. The empirical results show a strong and

statistically significant long-run relationship between budget deficits and seignorage, and

between money creation and inflation. The long run inflationary impact of the deficit stays

its course even when the model takes into account output growth or velocity. Mokoto

Richard and Ndedzu Desmond (2012) did a study along similar lines and found government

deficits to be highly inflationary under excess Central bank credit to government in the case

of Zimbabwe. They concluded that when modelling fiscal dominance-monetary

accommodation hypothesis as a Vector Autoregressive model the inflationary worries are

not present if government settles its debt procured through money creation and if the

government restricts institutional credit.

The external balance appears as a variable of interest for the monetary authorities as it

features in the frame work for fiscal-monetary policy mix in figure 1. Foued Chihi and Michel

Normandin (2008) found that the covariance of the external and budget deficit is

numerically positive for 24 developing countries examined and is statistically significant for

almost all cases. They also found similar results from the estimated correlation between

external balance and budget deficits, and the estimated slope coefficient obtained by

regressing the external deficit on a constant and the budget deficit to ascertain causality,

which may not be concluded firmly from covariance analysis.

17

Raghbendra Jha (2007) observes that with poor credit and bond markets and downwardly

inflexible fiscal expenditures, some of the financing of the resultant deficit spills over onto

the external sector and the central bank.

From the manipulation of the national accounts two gap model one can deduce that fiscal

policy has indeed an impact on the external position where;

Y= C+ G+I+X-M --------------------------------(i)

Y-C = G+I+X-M

Y- C -G = S

S=I+X-M

S-I = X-M-----------------------------------------(ii)

Thus if government saving fall S will fall and it will be reflected in the external account,

more so if the reduction in savings are a result of a draw down in foreign reserves.

Therefore a government deficit is likely to have a negative impact on the external sector.

Olivier Blanchard (2004) looks at fiscal dominance when fiscal dominance has been

instigated by increased interest rates that make government debt more attractive. He

argues that if increased government debt due to increased interest rates increases the

probability of default on the debt then this may lead to a real depreciation in the exchange

rate and higher inflation. Therefore Olivier concluded that this could have dire

consequences for inflation targeting in Brazil. He ultimately concluded for Brazil in 2002 and

2003 that an increase in the real interest rate in response to higher inflation leads to a real

depreciation and the real depreciation leads in turn to further increases in inflation. Olivier

presents a model between the interest rate, the exchange rate, and the probability of

default, in a high-debt high-risk-aversion economy such as Brazil in 2002 and 2003. Jean

Nachega (2005) noted that in the case of the DRC. Beaugrand (1997) and Akitoby (2004)

estimated the fiscal deficit-inflation relationship in a single equation and failed to account

for potential feedback from inflation which is taken care of by Nachega (2005) by using a

VAR with inflation feedback effect which Blanchard (2004) explained through increased

interest as responding to deficit driven high inflation. As the interest rates respond to

18

inflationary pressures the deficit increases all the more with high interest rates as

government debt is made more attractive leading to a vicious cycle.

Sargent and Wallace (1981) present a dynamic analytical mathematical framework showing

the interactions that shows that even in an economy that satisfies monetarist assumptions 2,

if monetary policy is interpreted as open market operations then monetary policy cannot

permanently control inflation. The extent to which inflation can be controlled depends on

the way fiscal and monetary policies are coordinated. Sargent and Wallace consider two

extreme forms of coordination; one where monetary policy dominates fiscal policy by

independently setting policy such as announcing growth rates for base money for the

current period and all future periods. The monetary authorities therefore determine the

amount of revenue it will finance the government deficit through seigniorage. The fiscal

authorities are then left with the bond financing ceiling imposed by the appetite for

government bonds of the public. Under this coordination the monetary authorities can

control inflation permanently because it can choose the growth of the money base which is

closely linked to inflation under the monetarist assumptions.

But in an instance where fiscal policy dominates monetary policy; that is; the fiscal authority

independently sets its budget, announcing all future deficits such that they are determined

by how much they are going to finance the deficit from either sale of government bonds or

seignorage. Sargent and Wallace observe that under this set up the monetary authorities

face the constraints imposed by the demand for government bonds, for it must try to

finance with seignorage any discrepancy between the revenue demanded by the fiscal

authorities and the amount of bonds that can be sold to the public. Under such

circumstances there is highly likely to be runaway inflation as monetary authorities are

helpless in controlling inflation with monetary policy being ultimately determined by fiscal

policy.

6.2.2 Fiscal Policy Impact on Monetary Policy in Fixed Exchange Rate Regimes

Under a fixed exchange rate regime fiscal policy tends to be restrictive and therefore

eliminates the money creation route of deficit financing. By 1973, most major world 2 The monetarist assumptions of a monetary economy are that: the monetary base is closely connected to the price level, and the monetary authorities can raise seignorage, which is revenue raised through money creation.

19

economies had migrated to freely floating exchange rates against the dollar. The transition

to freely floating exchange rates was characterized by plummeting stock prices, skyrocketing

oil prices, bank failures and inflation. Buchanan and Wagner (1977) argued that deficits are

inflationary under an interest-rate pegging monetary policy for industrialised countries. This

is due to the non-flexibility of the interest rates where the Central bank in the desire to hold

down interest rates and thus peg at a low point under prevailing economic conditions

purchase government bonds and in the process increase base money.

The smaller economies continued with the pegged exchange rate system for various reasons

including the size and openness of their economies to trade and financial flows, the

structure of production and exports, stages of its financial development, its inflationary

history, and the nature of the shocks they faced. Industrialized economies adopted explicit

inflation targeting, along with floating exchange rate systems in the 1990s and the policy has

spread to emerging and developing economies and the industralised economies have

moved further to an ultimate fixed exchange rate system under a monetary union.

Ieva Sakalauskaite (2010) found empirical evidence to the fact of inferior public balances in

countries operating under currency pegs, and the argument that the changes in economic

conditions after a fixed exchange rate regime is established may create expansionary

temptations for politicians, resulting in lower surpluses. Ieva (2010) in his research though

finds that rigid exchange rate arrangements are conducive to delivering fiscal discipline,

while the effects of currency boards do not differ significantly from those of regular pegs. As

earlier pointed out developing countries opted for fixed exchange rate regimes for many

reasons one of which was historically high inflation figures which were to be tamed by fixed

exchange rate regimes in instilling fiscal discipline and importing low inflation. Typical the

country/currency chosen to be pegged to ought to exercise both fiscal and monetary

discipline hence low and stable inflation.

Swaziland has been on a fixed exchange rate system since 1974 on the establishment of the

Central Bank and issuance of the first notes and coins of the local currency, the Lilangeni.

Swaziland is a small landlocked country with an open economy and South Africa is her

dominant trading partner, and has a low level of financial development such as the low level

of activity in the stock exchange which factors have contributed to Swaziland adopting a

20

fixed exchange rate system. Countries with open unbiased economies and developed

financial markets as earlier observed opted for floating exchange rate systems.

Swaziland’s membership to the Common Monetary Area agreement cements the

commitment by Swaziland to exercise monetary and fiscal disciplines.

Ieva Sakalauskaite (2010) observed for 10 Central and Eastern European transition countries

over the period 1992-2008 that rigid exchange rate arrangements are conducive to

delivering fiscal discipline, while the effects of currency boards do not differ significantly.

The first generation model following the seminal work of Paul Krugman (1979) and Maurice Obstfeld (1986). A standard first-generation model of a small open economy in log notations

m-p = -α(i)…………………………..(1) Money Market Equilibrium

m = d + r………………………….. (2) Money Supply

p = p* + e………………………... (3) Purchasing Power Parity

i = i* +é…………………………. (4) Uncovered Interest Rate Parity

i:domestic-currency interest rate i*:foreign-currency interest rate

p:domestic price level p*:foreign price level

e:nominal exchange rate é:expected and actual rate of exchange rate change

r:international reserves m:domestic supply of money

m-p:real money balances

Equation 1 can be transformed to analyse the economic mechanism of inflationary effects

when discount rate in Swaziland is not equated to the discount rate in South Africa.

Substituting from equation 4 to equation 1;

i-i*=é………………………………………4(a)

m-p = -α(i*+é)……………………………..(5)

21

Equation 6: when domestic currency interest rate greater that foreign currency interest rate (i>i*)

m-p = -αi* – α(+é)……………………………(6)

Substituting from equation 6 equation 2;

d+r-p = -αi* -α(+é)…………………………...6(a)

Not equating (higher domestic currency inflation)Swaziland’s discount rates to South

Africa’s results in the expected and actual rate of exchange rate change ( αé ) in equation 6

featuring in the determination of domestic prices with expected and actual rate of exchange

rate change negatively related to real money balances. Equation 6 and 6(a) show a scenario

where domestic currency interest rate is greater than foreign currency interest rate. As é

increases (expected or actual depreciation) the domestic price level increases and real

money balances (m-p) fall. The model is built such that a forward puzzle does not exist,

where increased interest rates result in an appreciation of the exchange rate. Higher

domestic currency interest rates lead a fall in domestic credit and to an expected or actual

depreciation pushing up domestic prices and leading to a fall in real money balances. A

sustained higher domestic currency interest rate thus ultimately leads to falling output a

depreciation of the local unit resulting in higher imported inflation. Higher inflation

increases the real deficit and the government financing requirement.

Equation 6(a): when domestic currency interest rates lower than foreign currency interest rate (i<i*)

m-p =-αi*-α(-é)……………………………….6(b)

Equation 6(b) shows a scenario where domestic currency interest rate is lower than the

foreign currency interest rate. Lower domestic interest rates lead to an expected or actual

depreciation in the exchange rate putting upward pressure on domestic prices and a fall in

real money balances. Fiscal deficit would increase as the exchange rate appreciates

reducing. This as an instance where the forward puzzle will hold for Swaziland as lower

interest rates are feared to instigate capital outflows putting pressure on international

reserves mounting and the fixed exchange rate regime as reserves are meant to upkeep the

local unit. Therefore, equating the domestic interest rate to that of South Africa helps

ameliorate price instability and risks to currency attacks.

22

6.2.2.1 Economic Mechanical Operation of the Fixed Exchange rate Regime.

Substituting from Equation 2, 3 and 4 into Equation 1:

d+r-p*-é = -α(i*+é)…………………………………. (7)

When the exchange rate is fixed at e=é it follows that é =0 and with p* and i* exogenous the

equation below will only adjust via international reserves( r) and domestic credit (d) to

maintain the fixed exchange rate. If r and d do not adjust to compensate for each other the

fixed exchange rate will be under pressure to collapse. In other words money supply should

be fixed and any observed increase in money supply should be backed by economic growth.

d+r-p*-é = -α(i*)……………………………………(8)

The only way to keep the exchange rate fixed is to keep money supply fixed by adjusting

either domestic credit (d) subject to the available level of international reserves. Removing

the exogenous variables p*, é and i* the following equation is obtained:

r=-d……………………………………………… 9(a)

d=-r……………………………………………...9(b)

Equation 9 implies that domestic credit and international reserves should adjust

interchangeable to keep the money stock fixed. An increase in the central bank’s domestic

assets must be offset by decrease in foreign assets according to the mathematical

relationship in equation 9. In simpler terms, when international reserves (r) decrease it

follows that domestic credit should fall to balance the system and maintain the peg.

Krugman (1979) observed that a standard crisis occurs in something like the following

manner. A country will have a pegged exchange rate; for simplicity assume that pegging is

done solely through direct intervention in the foreign market. At that exchange rate the

government’s reserves gradually decline. A parallel scenario is when the monetary

authorities do not intervene in the foreign exchange market but use the foreign currency as

legal tender alongside the local currency. Strong trade and close proximity to the country

pegged to supports the growth of foreign assets to buoy the peg and trade links will further

be strengthened by stable domestic economic condition that would not encourage strong

trade. Should there be instability that would increase the demand for foreign assets hence

23

capital flight. The government or rather the economy not being able to keep up with the

demand for foreign assets(capital flight) would resort to a loan to sustain the import

appetite in the process increase the deficit which would impact on the conduct of monetary

policy. Hence the government’s need to implement restrictive fiscal policy and ensure

adequate economic growth under a fixed exchange rate system to limit excessive money

creation which may be undesirable for the fixed exchange rate regime. This then brings the

monetary and fiscal authorities to desire to limit money creation subject to the constraint

imposed by the fixed exchange regime, which both parties need to maintain the peg. The

krugman and Obstfeld model first generation model demonstrate why fiscal authorities

would be encouraged to exercise fiscal restraint under a fixed exchange rate regime where

currency not backed by reserves would develop a breeding ground for a currency attack.

Fiscal authorities can therefore not set the deficit without taking in account the targets set

by monetary authorities of increases in base money and level of reserves that are

commensurate to the fixed exchange rate. Under a fixed exchange regime the monetary

policy has to dominate fiscal policy for the exchange rate regime to survive. Moreover there

is no desire for the monetary authorities to abandon interest rate tracking which is in line

with maintaining the peg. Abandoning interest rate tracking under a fixed exchange rate

regime is detrimental to the economy as explained in the scenarios of above and below

target interest rates.

Doose Toublaboe and Rory Terry (2013) observe that the inflation performance is generally

better under pegged regimes with annual inflation rate of 8 percent compared with 14

percent for intermediate regimes, and 16 percent for countries with floating regimes. An

even more compelling situation according to the authors is the evidence from the CFA franc

zone. According to Reinhart and Rogoff (2002) these countries, despite the relatively high

degree of exchange rate stability they enjoyed, constitute the region of the world that has

experienced by far the most frequent bouts of deflation- about 28 percent of the time(on

average ) for the period 1970-2001. A pegged regime by providing a clear and transparent

nominal anchor has the role of communicating to the public the monetary authorities’

commitment to prudent and sustained monetary policy and low inflation target according to

the authors. Loungany and Swagel (2001) with data from 1964 to 1998 for 53 developing

countries found that while inertia factors dominate the inflation process in developing

24

countries with fixed exchange rate regimes, monetary growth and exchange rate changes

are far more important in countries with floating exchange rate regimes.

7. METHODOLOGY AND DATA

The primary purpose of the study is to investigate the effects of fiscal policy on the conduct

of monetary policy by considering the impact of the budget deficit on monetary policy

variables such as inflation and the interest rates. The long and short run behavior of inflation

and interest rate differential is investigated by estimating a model in levels and an error-

correction model in a vector autoregressive (VAR) model to address the impact of the fiscal

deficit on monetary policy variables and issues of potential feedback from inflation and

nonfiscal real disturbances to the fiscal deficit. Subsample results are also run to determine

parameter constancy and time invariance under different economic conditions. Jean-Claude

Nachega (2005) estimated for the DRC the interplay between fiscal deficits and money

creation for two sample periods; first from 1981 to 1990, when inflation was relatively high,

with low but positive output growth and limited democratization and a period of relative

stability from 1991 to 2003. The Lucas critique (Lucas 1976) dismisses the stability of

parameters given changes in the economic environment and Swaziland is no exception

given that from 1980 to 1999 the inflation rate averaged 11.8 percent and from 2000 to

2003 inflation averaged 7.2 percent. Inflation moved from double digit to single digit. The

empirical investigation will start with the investigation of time-series properties of the data

to avoid spurious regression problems that may arise when statistical inferences are drawn

from non-stationary time series data. The empirical investigation then employs the

Johansen (1988) and Johansen and Juselius (1990) multivariate cointegration procedure to

determine empirically the number of cointegration vectors, the values of the adjustment

parameters, and the exogeneity status of each variable in the model.

When estimating a model that includes time series variables, the first thing to do is to make

sure that all the time series in the model are stationary or they are cointegrated which

means the model defines a long-run relationship among the cointegrated variables. The

cointegration test generally takes two steps. The first step is to conduct a unit root test on

each variable to determine the order of integration. If they are integrated of the same

order, the second step is to estimate the model and test whether the residual of the model

25

is stationary. The techniques that will be used as earlier mentioned is the Johansen

cointegration test in EViews and also estimate the error correction model using the Vector

Autoregression method. The data used in the estimation are sourced mainly from the

Central Bank of Swaziland quarterly publications and Central Statistics Office (CSO) data. The

variables to be included in the model from the strength of the literature and theoretical

review and research question are the GDP growth(gdpgr), the exchange rate(exrate), gross

international reserves(gir), the deficit as a percent of GDP(df), discount rate(dr), base

money(mb) and money supply (m1) and inflation (infl). A single equation model is

estimated to investigate the impact of the financial crisis and introduction of value added

tax on monetary policy. The equation to determine the precise direction of monetary policy

in Swaziland since Swaziland is in a Common Monetary Area and pursues a fixed exchange

rate regime is to run the fiscal variable (deficit) against the interest rate differential between

South Africa and Swaziland. Swaziland by virtue of pursuing a fixed exchange rate regime

tracks (to guard against capital flight detrimental to the pegged exchange rate regime)

South Africa’s discount rate with flexibility so as to sometimes respond to variables like

credit extension, GDP growth and inflation. . The impacts of the deficit and GDP growth on

the discount rate differential between South Africa and Swaziland is investigated using the

Johannes approach.

8. ECONOMETRIC RESULTS8.1 Unit-Root Tests

The first step of testing cointegration is to test all the time series variables for stationarity by

conducting the augmented Dickey-Fuller unit root test on each of the 8 series. The null

hypothesis is the presence of a unit root. An important practical issue for the

implementation of the ADF test is the specification of the lag length. If the lag length is too

small then the remaining serial correlation in the error will bias the test and if it is too large

then the power of the test will suffer. The lag length in the ADF regression is selected using

the Akaike information criterion. Venus Khim-Sen Liew (2004) found that the Akaike

information criterion is superior to the other criteria under study in the case of small sample

(60 observation and below), in the manner that it minimizes the chance of under estimation

while maximizing the chance of recovering the true lag length. If the absolute value of the t-

26

statistic for testing the significance of the last lagged difference is greater than 1.6 then set

the lag length there and perform unit root test. Otherwise reduce the lag length by one and

repeat the process. In the unit root test of levels the intercept and also time trend is

included. In the unit root test of first differences only the intercept without trend is

included.

Table 1. ADF Statistics for Testing for a Unit Root

Variables t-adf Lag Additional regressors

In Levels1. gir(gross international reserves)

-1.35 2 Constant +trend

2. exrate(exchange rate E/USD) -0.71 1 Constant + trend3. m1(money supply) 2.10 7 Constant + trend4. mb(base money)5. df(deficit as a percent of GDP)6. dr(discount rate)7. infl(inflation)8. SA inflation9. Deficit10. discount rate diff11. GDP growth12. Dummy VAT13. Dummy FCIn differences

3.18-3.83-3.28-0.17-1.51-3.83-1.86-2.04

--

83065884

Constant + trendConstant + trendConstant + trendConstant + trendConstant + trendConstant + trendConstant + trendConstant + trend

--

1. gir(gross international reserves)

-6.35*** 1 Constant

2. exrate(exchange rate E/USD) -4.54*** 1 Constant3. m1(money supply) 5.80*** 6 Constant4. mb(base money)5. df(deficit as a percent of GDP)6. dr(discount rate)7. SA inflation8. infl(inflation)9. discount rate diff10. GDP growth11. Inflation

2.97*-4.41***-7.43***-5.11***-5.64***5.68***

-3.62**-6.40***

1040

125

1031

ConstantConstantConstantConstantConstantConstantConstantConstant

The Augmented Dicky-fuller test finds that all variables are non-stationary at levels meaning

that they have a long run. The Johansen cointegration test in E-views and the error

correction model using the VAR is then employed to determine the long run and short run

relationship respectively.

27

8.2 THE LONG RUN INFLATION ESTIMATION EQUATION AND DIAGNOSTIC TESTS.

The Johansen procedure is applied to the non-stationary series, the seigniorage, money

supply, gross international reserves, inflation, discount rate, exchange rate and the deficit to

test for cointegration. The cointegration of the variables suggests that there is a long-run

relationship among the variables. Ramesh (2000) used the Johansen test uses the VAR

method, in which all cointegrating series are considered endogenous. The likelihood ratio

(LR) value is greater than the 5 percent critical value where it gives 3 cointegrating equation.

The trace statistics therefore shows three cointegrating equations at the 0.05 level. The

Max-egen value test indicates 2 cointegrating equation at the 0.05 level. The first

cointegrating equation is taken and it gives credence to economic sense. The normalised

coefficient table giving the estimate of the model (cointegrating equation) with all variables

are taken to the left side to change signs, hence equation 1 see appendix1. The models is

found to be unstable where upon variables suspected to be multicollinear are removed from

the model to get the following long run estimation.

EQUATION 8(i).

Johansen Cointgration E-views Output Log run

CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB CLSAINFL 1.000000 -0.256780 -0.344805  0.813924  0.016732 -0.076751  0.844468 -0.187731

 (0.12617)  (0.09316)  (0.07099)  (0.00795)  (0.20131)  (0.31249)  (0.08880)

Table 2. Lag length CriterionVAR Lag Order Selection CriteriaEndogenous variables: CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMBExogenous variables: CDate: 11/12/15 Time: 09:12Sample: 1981 2013Included observations: 31

 Lag LogL LR FPE AIC SC HQ

0 -142.5710 NA  0.000586  9.585228  9.862774  9.6757011 -17.11875  194.2487  1.92e-06  3.814113   5.756935*  4.4474242  35.73076   61.37363*   8.57e-07*   2.727048*  6.335145   3.903197*

28

 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

Figure3. AR ROOT Stability Tests

Money supply is and gross international reserves are dropped from the estimation to render

the model stable and the following results are obtained from the Johannes Cointegration

estimations.

Table 3. Johansen Unrestricted Cointegration Rank Test (Trace)

Hypothesized

No of CE(s)

Eigen value Trace

Statistics

0.05

Critical Value

Prob.**

None* 0.917752 164.9246 95.75366 0.0000

At most 1* 0.719539 89.98402 69.81889 0.0006

At most 2* 0.557740 51.84440 47.85613 0.0201

At most 3 0.423246 27.36865 29.79707 0.2203Trace test indicates 3 cointegrating eqn(s) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level**MacKinnon-Haug-Michelis (1999) p-values

29

The likelihood ratio (LR) value is greater than the 5 percent critical value where it gives 3

cointegrating equation. The trace statistics therefore shows three cointegrating equations at

the 0.05 level. The Max-egen value test indicates 2 cointegrating equation at the 0.05 level.

Table 4 Johansen Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hyphothesis No. of CE(s)

Eigenvalue Max-Eigen Statistics

0.05Critical Value

Prob.**

None* 0.917752 74.94055 40.07757 0.0000

At most 1* 0.719539 38.13963 33.87687 0.0146

At most 2 0.557740 24.47575 27.58434 0.1190

At most 3 0.423246 16.51016 21.13162 0.1964Max-eigenvalue test indicate 2 cointegrating eqn(s) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 levelMacKinnon-Haug-Michelis (1999) p-values

Inflation= -0.128*Fiscal balance + 0.395*LnSAinflation (0.0116) (0.1061) (11.044) (3.723)

-0.378*LnDR + 0.388*LnExrate - 0.337*LnMb-------------------Eqn...8(ii) (0.1475) (0.125) (0.087) (2.5627) (3.104) (3.874)

R2 = 0.837

Adj. R-sq = 0.729

F-statistics = 67.384

The figures in parenthesis are standard errors and all variables are found to be highly significant with t values greater than 2.

30

DIAGNOSTIC TESTS

Table 5 Diagnostic Test for the Inflation Equation.

Diagnostic Tests H0 Jarque-Bera Probability

Normality Residual is normally

distributed

143.6835 0.9836

Dependent variable Chi-sq Probability

Exogeniety Inflation 34.44540 0.0002

H0 Q-statistics Probability

LM Serial Correlation No serial correlation 33.3333 0.5961

H0 Chi-sq Probability

White

Heteroskedasticity(No

Cross Term)

No heteroskedasticity 507.2970 0.4504

Akaike information

Criterion

-2.72705 lag order 2 chosen by akaike information criterion

Schwarz 6.335145

Stability Tests

Roots of Characteristic PolynomialEndogenous variables: CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMBExogenous variables: CLag specification: 1 2Date: 11/11/15 Time: 15:54

     Root Modulus

 0.970610 - 0.027202i  0.970992 0.970610 + 0.027202i  0.970992 0.565203 - 0.621910i  0.840373 0.565203 + 0.621910i  0.840373-0.630287  0.630287-0.150617 - 0.597776i  0.616459-0.150617 + 0.597776i  0.616459-0.272598 - 0.528869i  0.594989-0.272598 + 0.528869i  0.594989 0.438361 - 0.286136i  0.523483 0.438361 + 0.286136i  0.523483-0.080844  0.080844

 No root lies outside the unit circle. VAR satisfies the stability condition.

31

Figure 4. AR Root Stability Test Inflation Equation 8(ii)

The model satisfies the stability condition.

Results Interpretation

A percentage in increase in the fiscal balance leads to a fall in inflation in the long run by

0.128 percent and a percentage increase in the exchange rate leads to a 0.388 percent rise

in inflation in the long run. The monetary base due to the fixed exchange rate regime and

the prevalent high states of liquidity tends to suppress inflation in the long run. This

inefficacy of monetary policy in a fixed exchange rate regime is cited in the literature review

by Loungany and Swagel (2001) in his study of 53 developing countries. The study by

Magnus Saxegaard(2006) also reinforces Loungany and Swagel (2001) for countries in high

liquidity like Swaziland where he found that excess liquidity weakens the monetary policy

transmission thus the ability of monetary authorities to influence demand conditions in the

economy. Base money though driven by a high fiscal deficit would have different effects on

the behaviour of inflation in the long run as it reduces liquidity.

In figure 4(i) excess liquidity is almost equivalent to base money

32

Graph 6(i). EXCESS LIQUIDITY

1990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014

0

500000

1000000

1500000

2000000

2500000

Swaziland

Base Money Excess liquidity

000

Graph 6(ii) Demonstration of Liquidity Pressures on Base Money

19811983

19851987

19891991

19931995

19971999

20012003

20052007

20092011

2013

-2500000

-2000000

-1500000

-1000000

-500000

0

500000

1000000

0

5

10

15

20

25

Mb less excess liquidity MbInflation

SOURCE:CBS QUARTERLTY REVIEW

The liquidity pressures have the tendency of dissipating the effects of base money on

inflation in the long run as shown in graph 4(ii) above. The increase in liquidity

reduces the velocity of money or rather it increases base money that is not for

transaction, precautionary or speculative purposes.

33

Graph 7 IMPULES RESPONSE FUNCTION and VARIANCE DECOMPOSITION OF THE LOG RUN EQUATION

34

Results Interpretation.

The inflation rate responds by rising and petering out in the tenth period to a negative shock

in the fiscal position. Inflation responds by falling to a positive shock in the discount rate

and it responds by increasing to a shock in the exchange rate to stabilise in the tenth period.

The discount rate responds by falling and then rising only to fall and stabilise in the tenth

period to a negative shock in the fiscal position. The monetary base responds by falling to a

shock in the discount rate and remains fairly stable to a deficit shock as government is not

apt to finance the deficit through money creation. The exchange rate shock lead to a rise in

the monetary base and the discount rate responds by increasing and then stabilising to a

shock in the inflation rate. The fall in the exchange rate leads to a deterioration in the fiscal

balance.

Therefore negative fiscal shocks lead higher inflation rate and the need to keep interest

rates on the high side though being mindful of the fixed exchange rate regime Swaziland is

pursuing and the negative implications high interest rates would have on growth. The

shocks in the fiscal balance may be induced by deterioration in the exchange rate position or

a fall in SACU receipts accompanied by high government expenditure. Due to high levels of

liquidity in the Swazi economy the monetary base has a muted effect on inflation as also

found by Magnus Saxegaard (2001) for countries in sub-Sahara Africa. He found that in both

the flexible and fixed exchange rate regime the price level dose not respond significantly to

unexpected changes in reserve money. Given the quantity theory of money where:

M*V=P*Q, M=money supplyV=velocityP=Price

35

Q=Real Output

Given that relationship, an increase in M might result in an increase in P or it might result in

an increase in Q or real output. Given the large amount of slack in the economy, most of it is

likely to find its way into Q not P. But due to high levels of liquidity base money gives rise to

a long run negative effect on inflation, meaning the increase in base money is not

adequately compensated with increases in velocity in an environment of involuntary excess

liquidity.

Graph 8.

198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010

0102030405060

Crude Estimation of the Trend for Velocity of Money

The velocity of money as crudely calculated by nominal GDP divided by base money shows a

dampening slope as the velocity of money in Swaziland stays less commensurate to the

increase in money supply creating involuntary excess liquidity which explains the effect of a

long term depression in prices due to increases in base money. The variance decomposition

shows that base money virtually has no effect in variance of the deficit and the discount rate

and has a huge variance effect on inflation.

8(iii) THE SHORT RUN INFLATION ESTIMATION EQUATION AND DIAGNOSTIC TESTS.

The short-run dynamics of inflation are estimated using e-views. In the Vector Error

Correction Model (VECM), short run association is deduced from the coefficient of the

differenced lagged variables. The coefficients in the cointegrating equation give the

estimated long-run relationship among the variables; the coefficient of the error term in the

VECM shows how deviations from that long-run relationship affect the changes in the

36

variable in the next period. Jean Claude Nachega (2005) yields the parsimonious SVECM by

deleting insignificant regressors.

8.3 The Short run Estimation of the Inflation Equation

Error

Correction

D(CLINFL

)

D(CLNDR) D(Cdeficit) D(CLNExrate) D(CLNMB) DCLSAINFL

CointEq1

Std Error

p-value

-0.3526

(0.3094)

(-1.1394)

0.1740

(0.1746)

(0.9967)

-0.7312

(2.4827)

(-2.9451)

0.2102

(0.1147)

(1.8319)

-0.0033

(0.0779)

(-0.0421)

0.2569

(0.2420)

(1.0616)

-Johansen Cointegration Output Short Run Estimation Equation.

Table 4 indicate that ECT in all equations has the negative and positive sign. The inflation

rate, fiscal balance and money supply are all functions of equilibrium in the cointegrating

relationship with the discount rate, South Africa inflation and base money being

disequilibrating functions. The fiscal balance, inflation and money supply have equilibrating

effects on the error term of inflation with only the fiscal balance having a significant error

correction terms. The system is stable with a sum of the coefficient of -0.446 with the fiscal

balance by its significance being critical to stabilisation of inflation.

The test for the presence of a short run relationship in the variables is done with the block

exogeniety Wald Causality Test.

Granger causality is done to see the short run causality running from independent variables

to dependent variables. It is found that the test statistics for granger test should follow the

chi-square distribution thus the results in e-views show the p value greater than 0.05.

37

Table 6(i). Block Exogeneity WALD CAUSALITY TESTS short run: Inflation Dependent Variable

Independent Variable Chi-sq. df. InflationDeficit 1.692092 2 p-value = 0.4291

Exchange rate 4.384557 2 p-value = 0.1857

Base Money 2.827884 2 p-value = 0.6279

South Africa Inflation 3.366878 2 p-value = 0.1117

Discount rate 0.930686 2 p-value = 0.2432

All 15.59706 10 0.118

Table 6(ii) Block Exogeneity WALD CAUSALITY TESTS: Fiscal balance

Dependent Variable

Independent Variable Chi-sq. df. Fiscal Balance

Inflation 8.640843 2 p-value=0.0133

South Africa Inflation 16.36501 2 p-value=0.0003

Discount Rate 5.173644 2 p-value=0.0753

Exchange rate 2.105563 2 p-value=0.3490

Monetary Base 3.196265 2 p-value=0.2023

33.60322 10 0.0002

Since all values in the table 4(i) are not significant they then all do not have a short run

relationship. Which means the deficit, exchange rate, base money, South Africa inflation and

the discount rate do not granger cause inflation in the short run or rather they are weakly

exogenous. Inflation, South Africa inflation and the discount rate granger cause the fiscal

balance in the short run through high repayments in with higher discount rates, and higher

real deficits through higher South Africa and domestic inflation in the short run.

The fiscal balance, the exchange rate, base money, South Africa inflation and discount rate

do not have a short run impact on inflation. Inflation, South Africa inflation and the discount

rate all have short run impact on the fiscal balance.

38

8.4 THE LONG RUN DISCOUNT RATE DIFFERENTIAL ESTIMATION EQUATION AND DIAGNOSTIC TESTS.

The monetary authorities conduct monetary easing by lagging the discount rate from that of

South Africa particularly when domestic growth prospects are grim. The below vector

autoregressive model estimates the impact of the deficit and other variables to discount

rate differential from that of South Africa.

The likelihood ratio (LR) value is greater than the 5 percent critical value where it gives 2

cointegrating equations. The trace statistics show two cointegrating equations at the 0.05

level. The max-eigen value test indicates 2 cointegrating equations at the 0.05 level.

The long-run equation estimation

Discount differential =Dd

Fiscal Balance = FB

GDP growth = GDPGr

INF =Inflation

Monetary base=Mb

The variables are cantered and lodged.

Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)

Eigen value TraceStatistics

0.05Critical Value

Prob.**

None* 0.880858 140.1625 69.81889 0.0000At most 1* 0.612328 76.33921 47.85613 0.0000At most 2* 0.529998 47.91133 29.79707 0.0002At most 3* 0.433321 25.26077 15.49471 0.0013At most 4* 0.239717 8.221919 3.841466 0.0041Trace test indicates 5 cointegrating eqn(s) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level**Mackinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

39

HypothesizedNo. of CE(s)

Eigenvalue Max-EigenStatistics

0.05Critical Value

Prob.**

None* 0.880858 63.82329 33.87687 0.0000At most 1* 0.612328 28.42788 27.58434 0.0389At most 2* 0.529998 22.65057 21.13162 0.0303At most 3* 0.433321 17.03885 14.26460 0.0177At most 4* 0.239717 8.221919 3.841466 0.0041Max-eigenvalue test indicates 5 cointegrating eqn (n) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level**Mackinnon-Haug-Michelis (1999) p-values

Dd = 0.11*FB +0.18*GDPGr –0.38*Mb+1.31*INF …….Eqn...8(iii)Std Errors (0.04) (0.08) (0.54) (0.17) t-value (2.54) (2.37) (2.45) (2.21)

DIAGNOSTIC TESTS

Table 7 DIAGNOSTIC TESTS FOR DISCOUNT DIFFERENTIAL EQUATION

Diagnostic Tests H0 Jarque-Bera ProbabilityNormality Residual is normally

distributed2.074557 0.9957

Dependent variable Chi-sq Probability

Exogeniety Discount rate differential 25.18214 0.0014

H0 Q-statistics Probability

LM Serial Correlation No serial correlation 24.29926 0.561325

H0 Chi-sq Probability

White

Heteroskedasticity(No

Cross Term)

No heteroskedasticity 284.3936 0.7328

Akaike information

Criterion

12.45074 lag order 2 chosen by akaike information criterion

Schwarz 14.99491

Table 7 Discount Differential Equation Stability Tests

Roots of Characteristic PolynomialEndogenous variables: CSASDDIFF CDEFICIT

40

CGDPGR CLNMBExogenous variables: CLag specification: 1 2Date: 11/12/15 Time: 11:09

     Root Modulus

 0.984472  0.984472 0.625941 - 0.447823i  0.769641 0.625941 + 0.447823i  0.769641 0.210650 - 0.591834i  0.628204 0.210650 + 0.591834i  0.628204-0.528342  0.528342-0.157332 - 0.453437i  0.479957-0.157332 + 0.453437i  0.479957

 No root lies outside the unit circle. VAR satisfies the stability condition.

Graph 8 AR root Stability Test Discount Rate Differential Equation.

41

19811984

19871990

19931996

19992002

20052008

2011

-5

0

5

10

15

20

25

InflationSA less SD Discount rate

As the fiscal balance improves the discount rate differential widens and it is significant;

improvements in the fiscal balance may reduce domestic discount rate in that there is a

relief on inflationary pressures. When GDP growth rises the discount rate differential closes

representing a tightening of monetary policy and the variable is significant attesting to the

fact that monetary authorities do exercise an accommodative monetary policy with

reference to the peg when economic growth is not favourable. The monetary authorities

used interest rate difference from that of South Africa during the financial crisis as a

stimulus measure to low economic growth. An increase in the monetary base leads to a

close in the discount rate differential depicting tight monetary policy in avoidance of capital

outflows and pressure on the exchange rate peg. The monetary base is highly significant

attesting to the monetary authorities’ commitment to the fixed exchange rate.

42

Graph 10 IMPULSE RESPONSE FUNCTION Discount Differential Equation (run new innovations)

Results Interpretation

The discount rate differential between South Africa and Swaziland responds by closing down

to a shock in the monetary base to curtail capital outflows where a the monetary base is

43

rarely influenced by the fiscal deficit. The discount rate responds by falling to widen the

discount rate differential and then rising to close the differential to positive shock in the

fiscal balance. When the fiscal balance is healthy there are less inflationary pressures and

monetary authorities can afford to loosen monetary policy but should tighten towards the

tenth period.

Table 8.5 THE SHORT RUN EQUATION ON DISCOUNT RATE DIFFERENTIAL

Error

Correction

D(CSASDDIFF

)

D(CDEFICIT) D(CGDPGR) D(CLNINFL) D(CLNMB)

CointEq1 -0.0820929

(0.01467)

(-5.596)

-0.096297

(0.08521)

(-1.13742)

-0.151226

(0.046797)

(-2.46003)

0.019106

(0.09061)

(0.21087)

-0.027248

(0.01693)

(-1.60992)

The discount rate differential, the fiscal balance, GDP growth and base money are all

equilibrating functions with GDP growth and discount rate differential being significant. The

Wald statistics below confirm the error correction term results above. The GDP growth

therefore can be regarded as having a short run impact on the discount rate differential with

the support of the Wald tests and error correction term results. The monetary authorities

have a tendency of setting the domestic discount rate lower than that of South Africa when

there is exceptionally low economic growth in Swaziland.

Table 9 Block Exogeneity WALD CASUALITY TEST : Discount rate differential

Dependent VariableIndependent Variable Discount rate differentialFiscal balance p-value = 0.0584

GDP growth p-value = 0.0001

Swaziland Inflation p-value = 0.2153

Base Money p-value = 0.0047

The fiscal balance has no short run impact on interest rate differential between South African and

Swaziland but the performance of GDP and base money impact on the interest rate differential.

44

Swaziland usually keeps its discount rate lower than that of South Africa if GDP is not performing

well. Increases in base encourage a close in the differential to avoid capital flight.

8.6 THE SHORT RUN VALUE ADDED TAX AND FINANCIAL CRISIS ON INFLATION ESTIMATION EQUATION.

Due to the short time series not all variables can be run on a vector error correction model

thus a single equation is estimated to assess the impact of the financial crisis and

introduction of value added tax on inflation.

The financial crisis was preceded by a rise in inflation which saw interest rates rising and the

collapse of the housing subprime. The resultant fall in aggregate demand saw inflation fall.

The dummy variable for the financial crisis and value added tax have no cointegrating

relation with the other variables and can therefore not be included in the Johansen

procedure of estimation employed above hence their effect on inflation is investigated

through the estimation of a single equation below.

Table 10 Results of the Financial Crisis and Value Added Equation.Dependent Variable: DCLNINFMethod: Least SquaresDate: 09/14/15 Time: 09:11Sample (adjusted): 1984 2013Included observations: 30 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

DCLNM1(-2) 0.363016 0.658516 0.551264 0.5879DCLNMB(-1) 0.243682 0.679348 0.358699 0.7238

DCLNGIR 0.222979 0.322777 0.690814 0.4980DCLNEXRATE(-1) 0.226699 0.515387 0.439862 0.6650

DCLNDR(-2) -0.253979 0.358906 -0.707648 0.4878CDEFICIT -0.006445 0.020399 -0.315956 0.7555

DCLSAINFL 0.255887 0.220343 1.161314 0.2599ECT(-1) -0.937731 0.225878 -4.151489 0.0005DUMFC -0.191652 0.294911 -0.649866 0.5236

DUMVAT 0.356936 0.361863 0.986383 0.3363C -0.151810 0.138017 -1.099935 0.2851

R-squared 0.707487    Mean dependent var -0.043255Adjusted R-squared 0.553533    S.D. dependent var 0.460908S.E. of regression 0.307970    Akaike info criterion 0.758946

45

Sum squared resid 1.802065    Schwarz criterion 1.272719Log likelihood -0.384193    Hannan-Quinn criter. 0.923306F-statistic 4.595444    Durbin-Watson stat 1.768183Prob(F-statistic) 0.002121

DIAGNOSTIC TESTS

Table 10 Diagnostic Results Value Added Tax and Financial Crisis Equation.

Diagnostic Test H0 F-Statistics ProbabilitySerial Correlation No serial Correlation 0.23914 0.7899

nR2 Chi-sq

Heteroskedasticity

Breuch-Pagan-

Godfrey

No Heteroskedasticity 1% 17.59929 23.20925

No Heteroskedasticity 5% 17.59929 18.30704

H0 Jarque-Bera Probability

Normality Residual are normally distributed 0.218220 0.896632

Durbin Watson 1.82˞

The single equation has an R2 of 70 percent and a durbin Watson statistics of approximately

2. The Breusch-Pagan-Godfrey test shows that there is an absence of heteroskedasticity

since the nR2 value of 17.6 is lower than the 1% critical chi-sq value of 23.20925, even the

5% critical chi-sq value of 18.30704 we fail to reject the null hyphothesis of no

heteroskedasticity. The presence of serial correlation is also rejected with p values of

greater than 5 percent recorded. Multicollinearity is solved by centering the variables.

Figure 11 -Value Added Tax and Inflation

46

Jan-05

May-05

Sep-05

Jan-06

May-06

Sep-06

Jan-07

May-07

Sep-07

Jan-08

May-08

Sep-08

Jan-09

May-09

Sep-09

Jan-10

May-10

Sep-10

Jan-11

May-11

Sep-11

Jan-12

May-12

Sep-12

Jan-13

May-13

Sep-13

-4-202468

10121416

Swaziland-inflation Polynomial (Swaziland-inflation)South Africa-inflation Polynomial (South Africa-inflation )Series3

The government of Swaziland in 2012 introduced value added tax which did not result in

change in the trajectory of inflation but instead the deviation of local inflation from that of

South Africa was a bit pronounces. The inflation did not experience a shock that resulted

prior to the financial crisis. The value added tax resulted in a temporal upward movement

in inflation of 0.428 percent in 2012 and the financial crisis on the other hand resulted in a

fall in the inflation rate by 0.121 percent.

9. RECOMMENDATIONS AND POLICY CONCLUSIONS.

The significantly negative long run impact the fiscal balance has on inflation suggests that

persistent fiscal deficits would be inflationary. The persistently high deficits would further

result in persistently higher discount rates which would not auger well for economic growth.

A healthy fiscal balance would ease pressures on tight monetary policy and the domestic

discount rate would fall below that of South Africa. The Swaziland monetary authorities

pursue a fixed exchange rate regime where they generally track discount rate in South Africa

in their conduct of monetary policy. The authorities have a tendency of keeping the

domestic discount lower than that of South Africa when there is a pressure for an

accommodative monetary policy due weak growth. The fiscal balance which relies on South

African Custom receipts for a healthy position deteriorates to push up inflation and the

discount rate in the long run compromising economic growth prospects. Exchange rate

deterioration also worsens the fiscal balance and results in a deficit which leads to higher

47

long term inflation. The monetary authorities should therefore discourage the government

from running persistent fiscal deficits by advocating a fiscal restructuring trimming the

bloated recurrent expenditure in particular the wage bill. The implementation of an

accommodative monetary policy is difficult under a huge budget deficit and the healthier

the fiscal position the better will be the long run discount rate trajectory. Fiscal shocks

should therefore be avoided by basically reducing the recurrent budget in particular the

wage bill and diversifying sources of revenue away from heavy reliance on SACU recipients.

The heavy reliance of government revenue on SACU receipts renders the fiscal balance

susceptible to shocks.

48

APPENDIX I. DEFINITIONS AND SOURCES OF VARIABLES

1. CLNINFL – domestic inflation2. CLNGIR – gross international reserves3. CLNEXRATE – ZAR/USD exchange rate4. CLNDR – domestic discount rate 5. CDEFICIT- fiscal balance6. CLNM1-money supply7. CLNMB-base money8. CLSAINFL-South Africa Inflation9. CGDPGR-Gross Domestic Product Growth10. CSASDDDIFF-South Africa, Swaziland discount rate differential

The variables have been centred as denoted by C and logged as denoted by LN save for those in percentages.

Source is the Central Bank of Swaziland Quarterly Report

APPENDIX II. TABLES AND FIGURES

TABLE AII1. COINTEGRATING EQUATION- Johansen Test; Long run equations -FOR INFLATION. VAR estimation and Diagnostic Tests.

Date: 09/04/15 Time: 09:36Sample (adjusted): 1983 2013

Included observations: 31 after adjustmentsTrend assumption: Linear deterministic trend

Series: CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB CLSAINFLLags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

49

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None *  0.961750  282.8591  159.5297  0.0000At most 1 *  0.916669  181.6869  125.6154  0.0000At most 2 *  0.682905  104.6541  95.75366  0.0106At most 3  0.592106  69.04890  69.81889  0.0575At most 4  0.403547  41.24969  47.85613  0.1809At most 5  0.388263  25.23030  29.79707  0.1534At most 6  0.271758  9.995273  15.49471  0.2811At most 7  0.005293  0.164514  3.841466  0.6850

 Trace test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level

 **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None *  0.961750  101.1722  52.36261  0.0000At most 1 *  0.916669  77.03283  46.23142  0.0000At most 2  0.682905  35.60516  40.07757  0.1465At most 3  0.592106  27.79921  33.87687  0.2229At most 4  0.403547  16.01939  27.58434  0.6641At most 5  0.388263  15.23502  21.13162  0.2728At most 6  0.271758  9.830759  14.26460  0.2233At most 7  0.005293  0.164514  3.841466  0.6850

 Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level

 **MacKinnon-Haug-Michelis (1999) p-values

 Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 4.340328 -1.114510 -1.496565  3.532695  0.072622 -0.333126  3.665270 1.487177  7.745601 -6.798221 -1.074260  0.260361  4.667609 -13.14691 3.327311 -3.522847  2.199235 -1.010841 -0.470988 -5.407373  10.06923-2.404371  2.270619 -1.778983  0.221049 -0.536756 -5.415453  4.292863-1.122585 -5.672100  0.675782  5.511216  0.007094  8.641862 -2.227827 0.889281 -6.821465 -0.491653 -1.210671 -0.094868 -14.52137  25.22772 0.570783 -1.666752  4.269467 -0.892476  0.010999  4.836908 -6.562962 0.479943  0.942051 -3.414547 -1.682066  0.203953  4.575122 -4.213899

 Unrestricted Adjustment Coefficients (alpha):

D(CLNINFL) -0.301636  0.002205 -0.036671  0.051236  0.031397  0.096352D(CLNGIR) -0.030826  0.082293 -0.017150  0.025527  0.028018  0.057185

D(CLNEXRATE) -0.003925  0.071600  0.005144  0.054760 -0.009524 -0.002526D(CLNDR)  0.007175  0.099560  0.018122 -0.003060 -0.069677  0.069337

D(CDEFICIT) -0.941203  2.043474  0.785595  0.378608  0.596192  0.432193D(CLNM1) -0.017025 -0.029107  0.006490 -0.019033  0.005694 -0.000741D(CLNMB)  0.009755 -0.015077 -0.021477 -0.018729  0.004271 -0.017358

D(CLSAINFL) -0.211346  0.188675  0.063868 -0.094742 -0.004277  0.047642

1 Cointegrating Equation(s): Log likelihood  121.6943

Normalized cointegrating coefficients (standard error in parentheses)

50

CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 -0.256780 -0.344805  0.813924  0.016732 -0.076751  0.844468

 (0.12617)  (0.09316)  (0.07099)  (0.00795)  (0.20131)  (0.31249)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.309199

 (0.22012)D(CLNGIR) -0.133795

 (0.13784)D(CLNEXRATE) -0.017035

 (0.10333)D(CLNDR)  0.031140

 (0.18462)D(CDEFICIT) -4.085130

 (3.35421)D(CLNM1) -0.073893

 (0.06234)D(CLNMB)  0.042338

 (0.07046)D(CLSAINFL) -0.917313

 (0.25076)

2 Cointegrating Equation(s): Log likelihood  160.2107

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000  0.000000 -0.543387  0.741740  0.024172  0.074324  0.389426

 (0.06969)  (0.06492)  (0.00733)  (0.18488)  (0.21813) 0.000000  1.000000 -0.773356 -0.281109  0.028973  0.588344 -1.772109

 (0.05915)  (0.05511)  (0.00622)  (0.15693)  (0.18515)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.305920  0.353252

 (0.23268)  (0.39686)D(CLNGIR) -0.011411  0.671765

 (0.12017)  (0.20497)D(CLNEXRATE)  0.089447  0.558958

 (0.08241)  (0.14056)D(CLNDR)  0.179204  0.763157

 (0.16778)  (0.28616)D(CDEFICIT) -1.046122  16.87691

 (2.89584)  (4.93914)D(CLNM1) -0.117180 -0.206475

 (0.05911)  (0.10081)D(CLNMB)  0.019916 -0.127652

 (0.07293)  (0.12439)D(CLSAINFL) -0.636720  1.696946

 (0.18596)  (0.31717)

3 Cointegrating Equation(s): Log likelihood  178.0133

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000  0.000000  0.000000 -1.151311 -0.166164 -1.442959  1.461353

 (0.41951)  (0.04743)  (1.19898)  (1.35033) 0.000000  1.000000  0.000000 -2.975327 -0.241915 -1.571074 -0.246527

 (0.62221)  (0.07034)  (1.77828)  (2.00277) 0.000000  0.000000  1.000000 -3.483799 -0.350275 -2.792269  1.972677

 (0.78829)  (0.08912)  (2.25294)  (2.53734)

51

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.427935  0.482437  0.355783

 (0.28382)  (0.42976)  (0.36558)D(CLNGIR) -0.068474  0.732181 -0.551030

 (0.14693)  (0.22247)  (0.18925)D(CLNEXRATE)  0.106564  0.540835 -0.469564

 (0.10160)  (0.15385)  (0.13087)D(CLNDR)  0.239502  0.699315 -0.647714

 (0.20604)  (0.31198)  (0.26539)D(CDEFICIT)  1.567798  14.10938 -10.75571

 (3.44271)  (5.21294)  (4.43442)D(CLNM1) -0.095586 -0.229339  0.237627

 (0.07257)  (0.10989)  (0.09348)D(CLNMB) -0.051545 -0.051992  0.040666

 (0.08609)  (0.13035)  (0.11088)D(CLSAINFL) -0.424212  1.471949 -0.825898

 (0.21571)  (0.32662)  (0.27784)

4 Cointegrating Equation(s): Log likelihood  191.9129

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000  0.000000  0.000000  0.000000  0.418554  4.510621 -5.413011

 (0.07920)  (1.62130)  (1.88837) 0.000000  1.000000  0.000000  0.000000  1.269167  13.81473 -18.01191

 (0.22356)  (4.57667)  (5.33055) 0.000000  0.000000  1.000000  0.000000  1.419045  15.22291 -18.82875

 (0.25809)  (5.28371)  (6.15406) 0.000000  0.000000  0.000000  1.000000  0.507871  5.171131 -5.970902

 (0.08767)  (1.79470)  (2.09033)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.551124  0.598774  0.264635 -1.019562

 (0.30053)  (0.43333)  (0.36678)  (0.18719)D(CLNGIR) -0.129850  0.790143 -0.596441 -0.174325

 (0.15587)  (0.22475)  (0.19024)  (0.09709)D(CLNEXRATE) -0.025100  0.665174 -0.566981 -0.083878

 (0.08227)  (0.11863)  (0.10041)  (0.05124)D(CLNDR)  0.246859  0.692367 -0.642271 -0.100603

 (0.22377)  (0.32266)  (0.27311)  (0.13938)D(CDEFICIT)  0.657485  14.96905 -11.42924 -6.230626

 (3.70495)  (5.34221)  (4.52178)  (2.30769)D(CLNM1) -0.049824 -0.272555  0.271485 -0.039643

 (0.07457)  (0.10752)  (0.09101)  (0.04645)D(CLNMB) -0.006513 -0.094519  0.073985  0.068226

 (0.09006)  (0.12986)  (0.10992)  (0.05610)D(CLSAINFL) -0.196416  1.256825 -0.657352 -1.034811

 (0.19673)  (0.28367)  (0.24010)  (0.12254)

5 Cointegrating Equation(s): Log likelihood  199.9226

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000  0.000000  0.000000  0.000000  0.000000 -1.181135  1.513872

 (0.35167)  (0.40285) 0.000000  1.000000  0.000000  0.000000  0.000000 -3.444200  2.992256

 (1.21653)  (1.39359) 0.000000  0.000000  1.000000  0.000000  0.000000 -4.074145  4.655830

52

 (1.31526)  (1.50668) 0.000000  0.000000  0.000000  1.000000  0.000000 -1.735215  2.434141

 (0.73974)  (0.84741) 0.000000  0.000000  0.000000  0.000000  1.000000  13.59862 -16.54956

 (3.04075)  (3.48331)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.586370  0.420690  0.285853 -0.846529 -0.031338

 (0.30246)  (0.50915)  (0.36462)  (0.32450)  (0.03690)D(CLNGIR) -0.161303  0.631220 -0.577507 -0.019910  0.013762

 (0.15375)  (0.25882)  (0.18535)  (0.16496)  (0.01876)D(CLNEXRATE) -0.014408  0.719197 -0.573418 -0.136368 -0.013527

 (0.08261)  (0.13906)  (0.09959)  (0.08863)  (0.01008)D(CLNDR)  0.325077  1.087581 -0.689357 -0.484607  0.019055

 (0.20661)  (0.34780)  (0.24907)  (0.22166)  (0.02521)D(CDEFICIT) -0.011791  11.58739 -11.02635 -2.944883 -0.105307

 (3.67698)  (6.18972)  (4.43267)  (3.94494)  (0.44865)D(CLNM1) -0.056216 -0.304851  0.275333 -0.008263 -0.001615

 (0.07540)  (0.12692)  (0.09090)  (0.08089)  (0.00920)D(CLNMB) -0.011307 -0.118743  0.076871  0.091763  0.016982

 (0.09136)  (0.15380)  (0.11014)  (0.09802)  (0.01115)D(CLSAINFL) -0.191616  1.281082 -0.660242 -1.058380  0.054517

 (0.19989)  (0.33648)  (0.24097)  (0.21445)  (0.02439)

6 Cointegrating Equation(s): Log likelihood  207.5401

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.089862

 (0.04088) 0.000000  1.000000  0.000000  0.000000  0.000000  0.000000 -1.160169

 (0.06947) 0.000000  0.000000  1.000000  0.000000  0.000000  0.000000 -0.256075

 (0.07225) 0.000000  0.000000  0.000000  1.000000  0.000000  0.000000  0.342117

 (0.05751) 0.000000  0.000000  0.000000  0.000000  1.000000  0.000000 -0.154677

 (0.52296) 0.000000  0.000000  0.000000  0.000000  0.000000  1.000000 -1.205628

 (0.03570)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.500686 -0.236569  0.238481 -0.963179 -0.040479 -1.096231

 (0.27507)  (0.54616)  (0.32900)  (0.29690)  (0.03349)  (0.83257)D(CLNGIR) -0.110449  0.241132 -0.605622 -0.089143  0.008337 -0.239403

 (0.13378)  (0.26561)  (0.16000)  (0.14439)  (0.01628)  (0.40490)D(CLNEXRATE) -0.016655  0.736430 -0.572176 -0.133309 -0.013287 -0.034484

 (0.08337)  (0.16553)  (0.09971)  (0.08998)  (0.01015)  (0.25233)D(CLNDR)  0.386737  0.614604 -0.723447 -0.568551  0.012477 -1.228106

 (0.18548)  (0.36827)  (0.22184)  (0.20019)  (0.02258)  (0.56139)D(CDEFICIT)  0.372549  8.639206 -11.23884 -3.468126 -0.146308  2.429518

 (3.66577)  (7.27847)  (4.38448)  (3.95659)  (0.44624)  (11.0953)D(CLNM1) -0.056875 -0.299798  0.275697 -0.007366 -0.001545 -0.002250

 (0.07615)  (0.15120)  (0.09108)  (0.08219)  (0.00927)  (0.23049)D(CLNMB) -0.026743 -0.000335  0.085405  0.112778  0.018628  0.432909

 (0.08912)  (0.17695)  (0.10659)  (0.09619)  (0.01085)  (0.26974)D(CLSAINFL) -0.149249  0.956096 -0.683666 -1.116058  0.049998  0.390001

 (0.19090)  (0.37904)  (0.22833)  (0.20605)  (0.02324)  (0.57781)

53

7 Cointegrating Equation(s): Log likelihood  212.4555

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000

 0.000000  1.000000  0.000000  0.000000  0.000000  0.000000  0.000000

 0.000000  0.000000  1.000000  0.000000  0.000000  0.000000  0.000000

 0.000000  0.000000  0.000000  1.000000  0.000000  0.000000  0.000000

 0.000000  0.000000  0.000000  0.000000  1.000000  0.000000  0.000000

 0.000000  0.000000  0.000000  0.000000  0.000000  1.000000  0.000000

 0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  1.000000

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.526721 -0.160543  0.043738 -0.922470 -0.040981 -1.316857

 (0.26887)  (0.53635)  (0.36783)  (0.29149)  (0.03260)  (0.83601)D(CLNGIR) -0.113324  0.249527 -0.627128 -0.084647  0.008281 -0.263767

 (0.13414)  (0.26759)  (0.18351)  (0.14542)  (0.01626)  (0.41708)D(CLNEXRATE) -0.024736  0.760026 -0.632620 -0.120674 -0.013443 -0.102962

 (0.08138)  (0.16234)  (0.11133)  (0.08822)  (0.00987)  (0.25303)D(CLNDR)  0.395379  0.589368 -0.658802 -0.582064  0.012644 -1.154870

 (0.18505)  (0.36914)  (0.25315)  (0.20061)  (0.02244)  (0.57537)D(CDEFICIT)  1.056004  6.643439 -6.126579 -4.536776 -0.133137  8.221227

 (3.28594)  (6.55493)  (4.49531)  (3.56235)  (0.39842)  (10.2170)D(CLNM1) -0.051538 -0.315382  0.315619 -0.015711 -0.001442  0.042977

 (0.07536)  (0.15032)  (0.10309)  (0.08169)  (0.00914)  (0.23430)D(CLNMB) -0.009939 -0.049407  0.211106  0.086502  0.018952  0.575316

 (0.07965)  (0.15889)  (0.10897)  (0.08635)  (0.00966)  (0.24767)D(CLSAINFL) -0.162294  0.994190 -0.781246 -1.095660  0.049746  0.279452

 (0.18905)  (0.37712)  (0.25862)  (0.20495)  (0.02292)  (0.58781)

Table A112 COINTEGRATING RESULTS OF DISCOUNT RATE DIFFERENTIAL LONG RUN EQUATIONDate: 12/04/15 Time: 14:49Sample (adjusted): 1984 2013Included observations: 30 after adjustmentsTrend assumption: Linear deterministic trendSeries: CSASDDIFF CDEFICIT CGDPGR CLNMBLags interval (in first differences): 1 to 2

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None *  0.806249  92.38297  47.85613  0.0000At most 1 *  0.494547  43.14747  29.79707  0.0008At most 2 *  0.379475  22.67847  15.49471  0.0035At most 3 *  0.243278  8.362798  3.841466  0.0038

 Trace test indicates 4 cointegrating eqn(s) at the 0.05 level

54

 * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None *  0.806249  49.23550  27.58434  0.0000At most 1  0.494547  20.46900  21.13162  0.0617

At most 2 *  0.379475  14.31567  14.26460  0.0491At most 3 *  0.243278  8.362798  3.841466  0.0038

 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

 Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

CSASDDIFF CDEFICIT CGDPGR CLNMB 0.954268  0.118537 -0.401090  0.411698 0.321945 -0.041128  0.601569  1.075057-0.718264  0.447443 -0.096965 -0.349599 0.230484  0.050277 -0.095753 -0.947634

 Unrestricted Adjustment Coefficients (alpha):

D(CSASDDIFF) -0.836907  0.236621  0.092588 -0.201602D(CDEFICIT) -1.477499  0.427782 -2.325510 -0.214817D(CGDPGR) -1.070391 -1.196546 -0.356268  0.461444D(CLNMB) -0.031039  0.040181  0.002585  0.026093

1 Cointegrating Equation(s): Log likelihood -138.2925

Normalized cointegrating coefficients (standard error in parentheses)CSASDDIFF CDEFICIT CGDPGR CLNMB

 1.000000  0.124218 -0.420312  0.431428 (0.05015)  (0.08352)  (0.16120)

Adjustment coefficients (standard error in parentheses)D(CSASDDIFF) -0.798633

 (0.14647)D(CDEFICIT) -1.409930

 (0.83561)D(CGDPGR) -1.021440

 (0.44658)D(CLNMB) -0.029619

 (0.01695)

2 Cointegrating Equation(s): Log likelihood -128.0580

Normalized cointegrating coefficients (standard error in parentheses)CSASDDIFF CDEFICIT CGDPGR CLNMB

 1.000000  0.000000  0.708084  1.864973 (0.22938)  (0.48638)

 0.000000  1.000000 -9.084013 -11.54058 (1.84517)  (3.91261)

55

Adjustment coefficients (standard error in parentheses)D(CSASDDIFF) -0.722454 -0.108936

 (0.14511)  (0.01808)D(CDEFICIT) -1.272208 -0.192732

 (0.87661)  (0.10921)D(CGDPGR) -1.406662 -0.077670

 (0.38668)  (0.04817)D(CLNMB) -0.016683 -0.005332

 (0.01544)  (0.00192)

3 Cointegrating Equation(s): Log likelihood -120.9002

Normalized cointegrating coefficients (standard error in parentheses)CSASDDIFF CDEFICIT CGDPGR CLNMB

 1.000000  0.000000  0.000000  0.891529 (0.14986)

 0.000000  1.000000  0.000000  0.947734 (0.66433)

 0.000000  0.000000  1.000000  1.374757 (0.35300)

Adjustment coefficients (standard error in parentheses)D(CSASDDIFF) -0.788957 -0.067508  0.469041

 (0.17638)  (0.06626)  (0.10402)D(CDEFICIT)  0.398123 -1.233265  1.075443

 (0.86345)  (0.32437)  (0.50920)D(CGDPGR) -1.150767 -0.237080 -0.255936

 (0.46461)  (0.17454)  (0.27399)D(CLNMB) -0.018539 -0.004175  0.036370

 (0.01895)  (0.00712)  (0.01117)

TABLE AII3.

WALD SHORT RUN CAUSALITY TEST FOR THE DISCOUNT RATE DIFFERENTIAL

Decision:There is no short run causality if the p values for the chi-square are greater than 0.05

C (1) =Deficit

Wald Test:Equation: EQ02CSASDDIFF

Test Statistic Value df Probability

t-statistic  1.886785  27  0.0700F-statistic  3.559958 (1, 27)  0.0700Chi-square  3.559958  1  0.0592

Null Hypothesis: C(1)=0Null Hypothesis Summary:

56

Normalized Restriction (= 0) Value Std. Err.

C(1)  0.136673  0.072437

Restrictions are linear in coefficients.

P values for the Chi-square is 0.0592 and is greater than 0.05 therefore the deficit has no short run impact on the discount rate differential between South Africa and Swaziland.

C(2)=GDP growth

Wald Test:Equation: EQ02CSASDDIFF

Test Statistic Value df Probability

t-statistic  1.913820  27  0.0663F-statistic  3.662707 (1, 27)  0.0663Chi-square  3.662707  1  0.0556

Null Hypothesis: C(2)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(2)  0.181627  0.094903

Restrictions are linear in coefficients.

P value for the Chi-square is 0.0556 and is greater than 0.05 therefore the GDP growth has no short run impact on the discount rate differential between South Africa and Swaziland.

C (3) =Money Supply M1

Wald Test:Equation: EQ02CSASDDIFF

Test Statistic Value df Probability

t-statistic -0.042893  27  0.9661F-statistic  0.001840 (1, 27)  0.9661Chi-square  0.001840  1  0.9658

Null Hypothesis: C(3)=0Null Hypothesis Summary:

57

Normalized Restriction (= 0) Value Std. Err.

C(3) -0.133523  3.112903

Restrictions are linear in coefficients.

P value for the Chi-square is 0.9658 and is greater than 0.05 therefore the Money supply (M1) growth has no short run impact on the discount rate differential between South Africa and Swaziland.

C (4) = Monetary base

Wald Test:Equation: EQ02CSASDDIFF

Test Statistic Value Df Probability

t-statistic  0.242883  27  0.8099F-statistic  0.058992 (1, 27)  0.8099Chi-square  0.058992  1  0.8081

Null Hypothesis: C(4)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(4)  0.861558  3.547222

Restrictions are linear in coefficients.

P value for the Chi-square is 0.8081 and is greater than 0.05 therefore the GDP growth has no short run impact on the discount rate differential between South Africa and Swaziland.

TABLEII 4 BLOCK/JOINT VAR WALD TEST FOR CAUSALITY

VEC Granger Causality/Block Exogeneity Wald TestsDate: 11/11/15 Time: 10:06Sample: 1981 2013Included observations: 30

Dependent variable: D(CSASDDIFF)

Excluded Chi-sq Df Prob.

D(CDEFICIT)  3.557674 2  0.1688

58

D(CGDPGR)  31.41483 2  0.0000D(CLNM1)  7.253863 2  0.0266D(CLNMB)  2.484321 2  0.2888

All  47.34844 8  0.0000

The deficit has no short run impact on discount rate differential between South Africa and Swaziland as the p value for the chi-square is greater than 0.05.GDP growth has a short run impact on the discount rate differential between South Africa and Swaziland as the p value for the chi-square is less than 0.05.Money Supply M1 has a short run impact on the discount rate differential between South Africa and Swaziland as the p value for the chi-square less than 0.05. This is basically driven by the perception taken by economic agents on an increase in the debt stock as government procures debt through the issuance of bonds. Or there fall in domestic credit to the private sector as reflected in M1 encourages the monetary authorities to lag effect a lower domestic discount rate to that of South Africa. Jointly Money Supply has a short run impact on the discount rate differential. The fall in credit to the private sector in

Monetary base Mb has no short run impact on the discount rate differential between South Africa and Swaziland as the p value for the chi-square is greater than 0.05.

TableAII 5 VAR heteroscedasticity test Results.VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)Date: 08/04/15 Time: 15:42Sample: 1981 2013Included observations: 31

   Joint test:

Chi-sq df Prob.

 813.2114 784  0.2280

   Individual components:

Dependent R-squared F(28,2) Prob. Chi-sq(28) Prob.

res1*res1  0.977925  3.164347  0.2684  30.31569  0.3483res2*res2  0.907536  0.701070  0.7429  28.13361  0.4574res3*res3  0.988483  6.130474  0.1497  30.64297  0.3332res4*res4  0.989231  6.561376  0.1407  30.66616  0.3321res5*res5  0.878937  0.518583  0.8358  27.24705  0.5048res6*res6  0.888686  0.570259  0.8084  27.54928  0.4885res7*res7  0.880778  0.527692  0.8309  27.30411  0.5017res2*res1  0.902307  0.659728  0.7629  27.97153  0.4660res3*res1  0.968238  2.177433  0.3636  30.01537  0.3625res3*res2  0.837640  0.368510  0.9163  25.96683  0.5749res4*res1  0.863107  0.450354  0.8727  26.75631  0.5315res4*res2  0.871209  0.483181  0.8549  27.00748  0.5178res4*res3  0.987226  5.520483  0.1647  30.60402  0.3349res5*res1  0.889618  0.575672  0.8055  27.57814  0.4870

59

res5*res2  0.915381  0.772687  0.7100  28.37680  0.4446res5*res3  0.901887  0.656592  0.7644  27.95849  0.4666res5*res4  0.980570  3.604856  0.2402  30.39768  0.3445res6*res1  0.608178  0.110870  0.9991  18.85352  0.9026res6*res2  0.790846  0.270083  0.9626  24.51622  0.6540res6*res3  0.632370  0.122866  0.9984  19.60346  0.8785res6*res4  0.756393  0.221783  0.9799  23.44818  0.7103res6*res5  0.662030  0.139917  0.9969  20.52292  0.8447res7*res1  0.978063  3.184655  0.2669  30.31995  0.3481res7*res2  0.917108  0.790281  0.7022  28.43036  0.4418res7*res3  0.900476  0.646276  0.7695  27.91477  0.4690res7*res4  0.959606  1.696868  0.4386  29.74779  0.3754res7*res5  0.962084  1.812459  0.4179  29.82462  0.3717res7*res6  0.954171  1.487144  0.4815  29.57929  0.3836

TableA116. Short run Inflation Equation (VECM) Vector Error Correction Estimates Date: 09/15/15 Time: 15:01 Sample (adjusted): 1984 2013 Included observations: 30 after adjustments Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1

CLNINFL(-1)  1.000000

CLNDR(-1)  0.230971 (0.10508)[ 2.19812]

CDEFICIT(-1)  0.088419 (0.00802)[ 11.0223]

CLNM1(-1) -0.049166 (0.26756)[-0.18375]

CLNMB(-1)  0.214946 (0.30049)[ 0.71532]

CLSAINFL(-1) -0.294681 (0.09263)[-3.18116]

60

C -0.038302

Error Correction: D(CLNINFL) D(CLNDR) D(CDEFICIT) D(CLNM1) D(CLNMB) D(CLSAINFL)

CointEq1 -0.538024  0.179879 -9.980481 -0.069273  0.077601  0.328223 (0.41438)  (0.24668)  (3.34109)  (0.08229)  (0.10530)  (0.38954)[-1.29838] [ 0.72920] [-2.98720] [-0.84178] [ 0.73699] [ 0.84259]

D(CLNINFL(-1)) -0.173622  0.061715  9.809106  0.051416  0.025613 -0.286290 (0.35655)  (0.21225)  (2.87478)  (0.07081)  (0.09060)  (0.33518)[-0.48695] [ 0.29077] [ 3.41212] [ 0.72613] [ 0.28271] [-0.85415]

D(CLNINFL(-2)) -0.220828  0.121614  5.452632 -0.039468 -0.080998 -0.256020 (0.27739)  (0.16513)  (2.23652)  (0.05509)  (0.07048)  (0.26076)[-0.79610] [ 0.73649] [ 2.43800] [-0.71645] [-1.14916] [-0.98183]

D(CLNDR(-1))  0.874727 -0.324784  12.19041  0.072141  0.036139  0.005206 (0.58402)  (0.34766)  (4.70883)  (0.11598)  (0.14840)  (0.54901)[ 1.49777] [-0.93419] [ 2.58884] [ 0.62199] [ 0.24352] [ 0.00948]

D(CLNDR(-2)) -0.577624 -0.302913  6.840904  0.031689  0.104917 -1.333585 (0.61860)  (0.36825)  (4.98766)  (0.12285)  (0.15719)  (0.58152)[-0.93376] [-0.82258] [ 1.37157] [ 0.25795] [ 0.66746] [-2.29328]

D(CDEFICIT(-1))  0.003212  0.011233  0.392399  0.001574  0.001445 -0.013562 (0.02082)  (0.01239)  (0.16784)  (0.00413)  (0.00529)  (0.01957)[ 0.15429] [ 0.90649] [ 2.33800] [ 0.38074] [ 0.27315] [-0.69305]

D(CDEFICIT(-2))  0.022547  0.010620  0.000108  0.008476 -0.003406  0.045264 (0.02218)  (0.01320)  (0.17884)  (0.00440)  (0.00564)  (0.02085)[ 1.01651] [ 0.80427] [ 0.00061] [ 1.92414] [-0.60427] [ 2.17084]

D(CLNM1(-1))  2.339843  0.358379  20.83563 -0.290251  0.392389  0.399152 (1.32388)  (0.78810)  (10.6742)  (0.26292)  (0.33640)  (1.24452)[ 1.76742] [ 0.45474] [ 1.95197] [-1.10397] [ 1.16644] [ 0.32073]

D(CLNM1(-2)) -0.159589 -1.049327 -4.991672  0.319899  0.228455 -0.402355 (1.18834)  (0.70741)  (9.58133)  (0.23600)  (0.30196)  (1.11710)[-0.13430] [-1.48334] [-0.52098] [ 1.35552] [ 0.75658] [-0.36018]

D(CLNMB(-1)) -0.301156 -0.210984  10.37846 -0.133790 -0.495143 -1.549630 (1.30894)  (0.77921)  (10.5537)  (0.25995)  (0.33260)  (1.23048)[-0.23008] [-0.27077] [ 0.98339] [-0.51468] [-1.48869] [-1.25937]

D(CLNMB(-2))  1.465060  0.925711  22.66034 -0.255734 -0.212321 -0.978215 (1.20104)  (0.71497)  (9.68377)  (0.23852)  (0.30519)  (1.12905)[ 1.21983] [ 1.29475] [ 2.34003] [-1.07217] [-0.69571] [-0.86641]

D(CLSAINFL(-1))  0.311144 -0.067816 -4.794115  0.016274 -0.080182  0.123189 (0.29386)  (0.17494)  (2.36936)  (0.05836)  (0.07467)  (0.27625)[ 1.05881] [-0.38766] [-2.02338] [ 0.27885] [-1.07380] [ 0.44594]

D(CLSAINFL(-2))  0.252790 -0.220189 -7.985972  0.063173  0.003583 -0.194175 (0.27354)  (0.16284)  (2.20553)  (0.05432)  (0.06951)  (0.25715)[ 0.92413] [-1.35219] [-3.62088] [ 1.16288] [ 0.05155] [-0.75511]

C -0.441224 -0.063411 -5.516866  0.192828  0.121570  0.217292 (0.25431)  (0.15139)  (2.05045)  (0.05050)  (0.06462)  (0.23907)[-1.73499] [-0.41886] [-2.69056] [ 3.81802] [ 1.88130] [ 0.90892]

 R-squared  0.640286  0.481192  0.783363  0.550203  0.489505  0.597197

61

 Adj. R-squared  0.348018  0.059661  0.607346  0.184742  0.074728  0.269920 Sum sq. resids  2.216069  0.785320  144.0644  0.087402  0.143086  1.958350 S.E. equation  0.372162  0.221546  3.000671  0.073909  0.094567  0.349853 F-statistic  2.190751  1.141535  4.450487  1.505506  1.180165  1.824744 Log likelihood -3.486218  12.07476 -66.10410  45.00843  37.61444 -1.631729 Akaike AIC  1.165748  0.128349  5.340274 -2.067229 -1.574296  1.042115 Schwarz SC  1.819640  0.782241  5.994166 -1.413337 -0.920404  1.696007 Mean dependent -0.043255 -0.033108  0.099667  0.142697  0.120907 -0.024758 S.D. dependent  0.460908  0.228466  4.788651  0.081856  0.098311  0.409449

 Determinant resid covariance (dof adj.)  1.55E-08 Determinant resid covariance  3.56E-10 Log likelihood  70.92328 Akaike information criterion  1.271781 Schwarz criterion  5.475373

TableA117 FINAL VAR STABLE FOR INFLATION EQUATIONDate: 11/12/15 Time: 09:53Sample (adjusted): 1984 2013Included observations: 30 after adjustmentsTrend assumption: Linear deterministic trendSeries: CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMBLags interval (in first differences): 1 to 2

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None *  0.917752  164.9246  95.75366  0.0000At most 1 *  0.719539  89.98402  69.81889  0.0006At most 2 *  0.557740  51.84440  47.85613  0.0201At most 3  0.423246  27.36865  29.79707  0.0929At most 4  0.291963  10.85849  15.49471  0.2203At most 5  0.016552  0.500726  3.841466  0.4792

 Trace test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

62

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None *  0.917752  74.94055  40.07757  0.0000At most 1 *  0.719539  38.13963  33.87687  0.0146At most 2  0.557740  24.47575  27.58434  0.1190At most 3  0.423246  16.51016  21.13162  0.1964At most 4  0.291963  10.35776  14.26460  0.1895At most 5  0.016552  0.500726  3.841466  0.4792

 Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

 Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB-4.346114 -0.558211  1.716699 -1.643488  1.684488 -1.465952 1.014829 -0.127408  4.756591  4.880303  2.247738  0.591197-8.020122 -0.007420  4.887953  1.395219 -0.937377  0.174645-4.978747  0.349602  2.740440 -5.514188  7.166552 -4.936033-1.488034  0.067189  2.970612  2.602021  0.858552  1.649104-1.549991  0.054254  2.950373 -4.004635 -1.169134  1.220616

 Unrestricted Adjustment Coefficients (alpha):

D(CLNINFL)  0.081126 -0.117760  0.163580  0.063451 -0.025419  0.005061D(CDEFICIT)  1.682415  0.042550 -0.165635 -0.055319 -1.194249 -0.019785D(CLSAINFL) -0.059105 -0.160845  0.019462 -0.012146 -0.057771  0.003799

D(CLNDR) -0.040038 -0.034961  0.008086  0.011355 -0.027537  0.018625D(CLNEXRATE) -0.048362  0.009476  0.065705 -0.033249 -0.006658  0.002276

D(CLNMB)  0.000754  0.030767 -0.017078  0.020011 -0.021451 -0.003586

1 Cointegrating Equation(s): Log likelihood  51.00518

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000  0.128439 -0.394996  0.378151 -0.387585  0.337302

 (0.01163)  (0.10605)  (0.14752)  (0.12518)  (0.08652)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -0.352585

 (0.30944)D(CDEFICIT) -7.311967

 (2.48272)D(CLSAINFL)  0.256877

 (0.24198)D(CLNDR)  0.174009

 (0.17459)D(CLNEXRATE)  0.210187

 (0.11474)D(CLNMB) -0.003279

 (0.07788)

2 Cointegrating Equation(s): Log likelihood  70.07500

63

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000  0.000000  2.174990  2.618807  0.928476  0.461327

 (0.48182)  (0.63693)  (0.54498)  (0.37888) 0.000000  1.000000 -20.00937 -17.44527 -10.24657 -0.965631

 (3.75239)  (4.96035)  (4.24427)  (2.95068)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -0.472091 -0.030282

 (0.28933)  (0.03712)D(CDEFICIT) -7.268785 -0.944564

 (2.54906)  (0.32702)D(CLSAINFL)  0.093647  0.053486

 (0.17187)  (0.02205)D(CLNDR)  0.138530  0.026804

 (0.17499)  (0.02245)D(CLNEXRATE)  0.219804  0.025789

 (0.11735)  (0.01505)D(CLNMB)  0.027945 -0.004341

 (0.07223)  (0.00927)

3 Cointegrating Equation(s): Log likelihood  82.31287

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000  0.000000  0.000000  0.435408  0.297733  0.082142

 (0.19514)  (0.18872)  (0.12550) 0.000000  1.000000  0.000000  2.641451 -4.443888  2.522769

 (1.71311)  (1.65679)  (1.10172) 0.000000  0.000000  1.000000  1.003866  0.289999  0.174338

 (0.20968)  (0.20279)  (0.13485)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.784024 -0.031496  0.378707

 (0.46169)  (0.02880)  (0.35378)D(CDEFICIT) -5.940375 -0.943335  2.280980

 (5.22839)  (0.32619)  (4.00638)D(CLSAINFL) -0.062438  0.053342 -0.771410

 (0.35062)  (0.02187)  (0.26867)D(CLNDR)  0.073683  0.026744 -0.195507

 (0.35939)  (0.02242)  (0.27539)D(CLNEXRATE) -0.307155  0.025301  0.283210

 (0.18844)  (0.01176)  (0.14440)D(CLNMB)  0.164915 -0.004214  0.064163

 (0.14328)  (0.00894)  (0.10979)

4 Cointegrating Equation(s): Log likelihood  90.56795

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000  0.000000  0.000000  0.000000  0.881165 -0.282934

 (0.27371)  (0.17415) 0.000000  1.000000  0.000000  0.000000 -0.904432  0.307995

 (1.28635)  (0.81846) 0.000000  0.000000  1.000000  0.000000  1.635145 -0.667372

 (0.35332)  (0.22481) 0.000000  0.000000  0.000000  1.000000 -1.339966  0.838469

 (0.32526)  (0.20695)

64

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -2.099929 -0.009313  0.552590 -0.829683

 (0.49845)  (0.03203)  (0.36032)  (0.36627)D(CDEFICIT) -5.664953 -0.962675  2.129380 -2.483424

 (5.94633)  (0.38206)  (4.29851)  (4.36948)D(CLSAINFL) -0.001966  0.049095 -0.804696 -0.593704

 (0.39762)  (0.02555)  (0.28744)  (0.29218)D(CLNDR)  0.017147  0.030714 -0.164388 -0.156154

 (0.40778)  (0.02620)  (0.29478)  (0.29964)D(CLNEXRATE) -0.141618  0.013678  0.192094  0.400740

 (0.19603)  (0.01260)  (0.14170)  (0.14404)D(CLNMB)  0.065287  0.002781  0.119001  0.014742

 (0.15441)  (0.00992)  (0.11162)  (0.11346)

5 Cointegrating Equation(s): Log likelihood  95.74683

Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000  0.000000  0.000000  0.000000  0.000000 -1.316215

 (0.55261) 0.000000  1.000000  0.000000  0.000000  0.000000  1.368560

 (0.60510) 0.000000  0.000000  1.000000  0.000000  0.000000 -2.584792

 (1.03361) 0.000000  0.000000  0.000000  1.000000  0.000000  2.409754

 (0.83971) 0.000000  0.000000  0.000000  0.000000  1.000000  1.172631

 (0.63051)

Adjustment coefficients (standard error in parentheses)D(CLNINFL) -2.062105 -0.011021  0.477079 -0.895825  0.151527

 (0.49900)  (0.03190)  (0.38377)  (0.38332)  (0.36910)D(CDEFICIT) -3.887871 -1.042915 -1.418269 -5.590883  1.663139

 (5.11476)  (0.32698)  (3.93367)  (3.92898)  (3.78332)D(CLSAINFL)  0.084000  0.045214 -0.976313 -0.744026 -0.615987

 (0.37163)  (0.02376)  (0.28582)  (0.28548)  (0.27489)D(CLNDR)  0.058123  0.028864 -0.246190 -0.227806 -0.095869

 (0.40545)  (0.02592)  (0.31182)  (0.31145)  (0.29990)D(CLNEXRATE) -0.131710  0.013230  0.172315  0.383416 -0.365752

 (0.19723)  (0.01261)  (0.15168)  (0.15150)  (0.14589)D(CLNMB)  0.097207  0.001340  0.055278 -0.041075  0.211426

 (0.14535)  (0.00929)  (0.11179)  (0.11165)  (0.10751)

Tables A118Deficit Testing for short-run effects

Wald Test:System: Untitled

Test Statistic Value Df Probability

Chi-square  1.034494  2  0.5962

65

Null Hypothesis: C(4)=0, C(5)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(4)  0.003212  0.020816C(5)  0.022547  0.022181

Restrictions are linear in coefficients.

Money Supply Tests for short run effectsWald Test:System: Untitled

Test Statistic Value Df Probability

Chi-square  3.191835  2  0.2027

Null Hypothesis: C(10)=0, C(11)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(10)  2.339843  1.323877C(11) -0.159589  1.188336

Restrictions are linear in coefficients.

Base Money/Seigniorage tests.Wald Test:System: Untitled

Test Statistic Value Df Probability

Chi-square  2.594742  2  0.2732

Null Hypothesis: C(12)=0, C(13)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(12) -0.301156  1.308941C(13)  1.465060  1.201041

Restrictions are linear in coefficients.

Discount Rate testsWald Test:System: Untitled

Test Statistic Value Df Probability

Chi-square  7.318879  2  0.0257

66

Null Hypothesis: C(4)=0, C(5)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(4)  0.874727  0.584019C(5) -0.577624  0.618600

Restrictions are linear in coefficients.

South Africa Inflation testWald Test:System: Untitled

Test Statistic Value Df Probability

Chi-square  1.191491  2  0.5512

Null Hypothesis: C(68)=0, C(69)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(68) -0.080182  0.074671C(69)  0.003583  0.069508

Restrictions are linear in coefficients.

Swaziland Inflation testsWald Test:System: Untitled

Test Statistic Value Df Probability

Chi-square  0.644310  2  0.7246

Null Hypothesis: C(2)=0, C(3)=0Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

C(2) -0.173622  0.356548C(3) -0.220828  0.277387

Restrictions are linear in coefficients.

Table A119. Exogeniety Tests.VAR Granger Causality/Block Exogeneity Wald TestsDate: 09/08/15 Time: 14:21Sample: 1981 2013

67

Included observations: 31

Dependent variable: CLNINFL

Excluded Chi-sq Df Prob.

CLNGIR  0.399401 2  0.8190CLNEXRATE  2.342018 2  0.3101

CLNDR  12.50601 2  0.0019CDEFICIT  1.610262 2  0.4470

CLNM1  3.128409 2  0.2093CLNMB  0.756612 2  0.6850

CLSAINFL  2.560628 2  0.2780

All  41.67178 14  0.0001

Dependent variable: CLNGIR

Excluded Chi-sq Df Prob.

CLNINFL  0.827352 2  0.6612CLNEXRATE  8.387982 2  0.0151

CLNDR  0.257251 2  0.8793CDEFICIT  7.351952 2  0.0253

CLNM1  0.379786 2  0.8270CLNMB  0.480863 2  0.7863

CLSAINFL  4.389646 2  0.1114

All  54.48748 14  0.0000

Dependent variable: CLNEXRATE

Excluded Chi-sq Df Prob.

CLNINFL  1.930395 2  0.3809CLNGIR  23.95439 2  0.0000CLNDR  1.220419 2  0.5432

CDEFICIT  6.576954 2  0.0373CLNM1  0.527702 2  0.7681CLNMB  2.174435 2  0.3372

CLSAINFL  8.963543 2  0.0113

All  51.26944 14  0.0000

Dependent variable: CLNDR

Excluded Chi-sq Df Prob.

CLNINFL  3.149171 2  0.2071CLNGIR  1.767480 2  0.4132

CLNEXRATE  4.520153 2  0.1043CDEFICIT  1.581335 2  0.4535

CLNM1  2.644291 2  0.2666CLNMB  0.746233 2  0.6886

CLSAINFL  3.390071 2  0.1836

All  22.40084 14  0.0707

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Dependent variable: CDEFICIT

Excluded Chi-sq Df Prob.

CLNINFL  0.699114 2  0.7050CLNGIR  1.830496 2  0.4004

CLNEXRATE  1.572106 2  0.4556CLNDR  4.207896 2  0.1220CLNM1  2.513337 2  0.2846CLNMB  1.330049 2  0.5143

CLSAINFL  3.855447 2  0.1455

All  21.21776 14  0.0962

Dependent variable: CLNM1

Excluded Chi-sq Df Prob.

CLNINFL  0.684351 2  0.7102CLNGIR  2.944836 2  0.2294

CLNEXRATE  7.442760 2  0.0242CLNDR  0.566650 2  0.7533

CDEFICIT  0.563306 2  0.7545CLNMB  0.709003 2  0.7015

CLSAINFL  3.566191 2  0.1681

All  16.40683 14  0.2892

Dependent variable: CLNMB

Excluded Chi-sq Df Prob.

CLNINFL  2.774556 2  0.2498CLNGIR  1.120270 2  0.5711

CLNEXRATE  2.305852 2  0.3157CLNDR  0.881585 2  0.6435

CDEFICIT  2.786904 2  0.2482CLNM1  6.672373 2  0.0356

CLSAINFL  2.755511 2  0.2521

All  20.62453 14  0.1116

Dependent variable: CLSAINFL

Excluded Chi-sq Df Prob.

CLNINFL  0.483881 2  0.7851CLNGIR  4.632949 2  0.0986

CLNEXRATE  7.893497 2  0.0193CLNDR  26.31771 2  0.0000

CDEFICIT  5.546403 2  0.0625CLNM1  1.963671 2  0.3746CLNMB  2.279033 2  0.3200

All  86.85901 14  0.0000

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Error Correction: D(CLNINFL) D(CLNDR) D(CDEFICIT) D(CLNEXRATE) D(CLNMB) D(CLSAINFL)

CointEq1 -0.352585  0.174009 -7.3119673  0.210187 -0.003279  0.256877 (0.30944)  (0.17459)  (2.48272)  (0.11474)  (0.07788)  (0.24198)[-1.13941] [ 0.99670] [-2.94515] [ 1.83189] [-0.04210] [ 1.06156]

Error Correction:D(CSASDDIFF

) D(CDEFICIT) D(CGDPGR) D(CLNINFL) D(CLNMB)

CointEq1 -0.820928 -0.969297 -1.151226  0.019106 -0.027248

 (0.14670)  (0.85219)  (0.46797)  (0.09061)  (0.01693)

[-5.59606] [-1.13742] [-2.46003] [ 0.21087] [-1.60992]

VEC Granger Causality/Block Exogeneity Wald TestsDate: 12/21/15 Time: 14:30Sample: 1981 2013Included observations: 30

Dependent variable: D(CSASDDIFF)

Excluded Chi-sq df Prob.

D(CDEFICIT)  5.679320 2  0.0584D(CGDPGR)  18.66710 2  0.0001

3 The adjustment is not in log form hence the error correction is actually 7.3 percent per period.

70

D(CLNINFL)  3.071373 2  0.2153D(CLNMB)  10.71137 2  0.0047

All  29.94782 8  0.0002

Dependent variable: D(CDEFICIT)

Excluded Chi-sq df Prob.

D(CSASDDIFF)  3.454565 2  0.1778D(CGDPGR)  1.960839 2  0.3752D(CLNINFL)  5.111677 2  0.0776D(CLNMB)  0.166280 2  0.9202

All  7.967012 8  0.4367

Dependent variable: D(CGDPGR)

Excluded Chi-sq df Prob.

D(CSASDDIFF)  1.670144 2  0.4338D(CDEFICIT)  1.560546 2  0.4583D(CLNINFL)  2.051138 2  0.3586D(CLNMB)  4.197033 2  0.1226

All  10.90148 8  0.2073

Dependent variable: D(CLNINFL)

Excluded Chi-sq df Prob.

D(CSASDDIFF)  1.320540 2  0.5167D(CDEFICIT)  0.025910 2  0.9871D(CGDPGR)  0.057768 2  0.9715D(CLNMB)  1.010148 2  0.6035

All  2.425579 8  0.9651

Dependent variable: D(CLNMB)

Excluded Chi-sq df Prob.

D(CSASDDIFF)  4.400633 2  0.1108D(CDEFICIT)  0.492037 2  0.7819D(CGDPGR)  2.097247 2  0.3504D(CLNINFL)  4.177608 2  0.1238

All  10.19312 8  0.2517

71

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