DEPARTMENT OF ECONOMICS

ISSN 1441-5429

DISCUSSION PAPER 26/16

Oil Curse and Finance-Growth Nexus in Malaysia: The Role of

Investment

Ramez Abubakr Badeeb, Hooi Hooi Lean* and Russell Smyth

Abstract: We empirically examine the existence of an oil curse in the finance-growth nexus in Malaysia.

We provide new insights into the oil curse phenomenon in Malaysia that challenges the

conventional argument that Malaysia is a counter-example of an oil curse country. We do not

find any significant evidence of direct effects of financial development on economic growth

and TFP. However, there are direct and positive effects on the level of investment due to

financial development and oil dependence. While we do not find statistical evidence of a direct

negative impact of oil rent on economic growth, our results reveal that the symptoms of an oil

curse exist. Specifically, we find that oil rent has a weak, indirect, impact on the finance-growth

nexus through the quantitative channel or investment quantity. The policy implications of our

findings are that the financial sector should be more involved in productive investment

activities that can strengthen its role in economic growth and that policymakers should reduce

dependence on oil and promote economic diversification.

Keywords: Oil Curse; Financial Development; Economic Growth; Investment; Malaysia.

JEL Classification: O13; O16; C22

* This version is forthcoming in Energy Economics. We thanks two referees and Jakob Madsen for

comments/advice on earlier versions of this paper.

Hooi Hooi Lean, Economics Program, School of Social Sciences Universiti Sains Malaysia

Ramez Abubakr Badeeb Economics Program, School of Social Sciences Universiti Sains Malaysia

Russell Smyth Department of Economics, Monash University, Australia

© 2016 Ramez Abubakr Badeeb, Hooi Hooi Lean and Russell Smyth

All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the prior

written permission of the author.

monash.edu/ business-economics ABN 12 377 614 012 CRICOS Provider No. 00008C

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1. Introduction

The relationship between natural resource (oil) dependence and economic growth has

received considerable attention among economists (Gelb, 1988; Auty, 1993; Sachs and

Warner, 1995, 1999, 2001; Gylfason, 2001; Mehlum et al., 2006; Apergis et al., 2014;

Apergis and Payne, 2014; Betz et al., 2015). In principle, oil and other non-renewable natural

resources should offer huge benefits to economies. However, empirical evidence shows that

these resources do not always lead to increased economic growth. This puzzling phenomenon

is referred to as the “natural resource curse” (Auty, 1993). The term “natural resource curse”

refers to the phenomenon of encumbering growth caused via a series of negative effects, due

to excessive dependence on natural resources, such as oil and natural gas, in a country or a

region (Shao and Yang, 2014). It occurs when natural resources interact with, and alter,

various social, political and economic factors, resulting in slower economic growth and

impeding development. “Oil curse” has often been used as a synonym for resource curse

because it is primarily observed in oil-dependent economies.

The literature on the natural resource curse consists of two strands. The first strand has

examined the direct effect of natural resource dependence on economic growth (e.g. Sachs

and Warner, 1995, 1999, 2001; Sachs, 2007; Gylfason, 2001; Mevrotas et al., 2011 and Kim

and Lin, 2015). The second strand of literature has examined the effect of natural resource

dependence on macroeconomic and political economic factors, such as human capital, good

governance, investment and savings that are associated with sustainable economic growth

(e.g., Atkinson and Hamilton, 2003; Gylfason and Zoega, 2006; Mehlum et al., 2006; Dietz et

al., 2007; Papyrakis and Gerlagh, 2007; Daniele, 2011; Blanco and Grier, 2012; Libman,

2013; Bhattacharyya and Hodler, 2014; Cockx and Francken, 2016).

4

One factor that is associated with sustainable economic development is financial

development. According to Levine (1997), there are two channels through which financial

development affects economic growth. These channels are depicted in Figure 1. The first

channel provides liquidity services and mobilizes savings, which affects the scale of

investment (quantitative channel). The second channel is the efficient allocation of resources

to productive investments, which induces economic growth (qualitative channel). Oil

dependence can influence the relationship between financial development and economic

growth in various ways. On the one hand, oil revenue might be considered an extra resource

for financial institutions, in which case the link between financial development and economic

growth would be stronger. On the other hand, high natural resource dependence may hinder

the ability of financial institutions to accumulate capital (quantitative channel) or to allocate

credit efficiently to productive investments (qualitative channel), which are negative effects.

[Insert Figure 1]

Empirical evidence concerning how natural resource dependence affects financial

development, and thus influences economic growth, is scant (Nili and Rastad, 2007; Beck,

2011; Yuxiang and Chen, 2011; Barajas et. al., 2013). Related research examines financial

sector characteristics in resource-dependent economies (Kurronen, 2015), the effect of oil

price fluctuations on the current account in oil exporting countries (Allegret and Mignon,

2014), and the effect of oil prices on financial markets (Narayan and Narayan, 2010; Kang et

al., 2014; Demirir et al., 2015; Phan et al., 2015). The latter, however, does not consider if

there is an oil curse, nor examine the effect of financial development on economic growth.

We examine the role of natural resource (oil) dependence on the finance-growth nexus in one

of the largest oil-rich economies in Southeast Asia, Malaysia. We first examine whether oil

5

dependence has a direct effect on economic growth. We then examine whether oil

dependence has an indirect effect on the finance-growth nexus via the investment quantity

(quantitative) and investment efficiency (qualitative) channels. While we do not find

evidence of a direct negative impact of oil dependence on economic growth, we do find that

oil dependence has a weak, indirect, impact on the finance-growth nexus through the

investment quantity channel. Our results are generally robust to several alternative ways of

measuring financial development as well as oil dependence.

We situate the study in Malaysia because there is considerable debate over the role of oil in

facilitating economic development in that country. Prior to the Asian financial crisis,

Malaysia was one of the fastest growing economies in the world and was widely considered

to be one of those countries that had escaped the resource curse (Gylfason, 2001; Rosser,

2006). However, recently, Doraisami (2015) has argued that over dependency on natural

resources has led the Malaysian government to engage in inefficient activities and provide

funding for unproductive investments, casting doubt on whether Malaysia really has escaped

the resource curse. This finding, along with the current destabilized economic situation in

Malaysia that is associated with the prolonged global fall in oil prices, motivates us to dig

deeper to explore empirically whether the conventional argument about Malaysia is still valid.

We contribute to the literature in the following important ways. First, as noted above, there

are very few papers that specifically examine how natural resource dependence affects the

relationship between financial development and economic growth and the associated

channels through which this occurs. We contribute to the scant evidence on the effects of oil

dependence on the relationship between financial development and economic growth and the

channels through which oil dependence affects the finance-growth nexus.

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Second, of the few studies that explicitly examine how natural resource dependence affects

the relationship between financial development and economic growth, almost all provide

cross-sectional and/or panel evidence for large numbers of countries (Nili and Rastad, 2007;

Beck, 2011; Barajas et. al., 2013). Beck (2011) has emphasized that understanding how

natural resource dependence influences the finance-growth nexus is critical for policymakers.

He states (at p.2):

“Policymakers who care about the development of their countries need to

understand the relative importance of different policy areas and the effectiveness of

specific policies. Understanding channels through which resource abundance can

stimulate or dampen economic development can be important to develop policies to

maximize the benefits of natural capital”.

Yet, findings from large multi-country studies are of limited value for policy-making in

specific countries. The use of cross-sectional or panel data has led previous studies to ignore

the existence of large variations among countries in terms of the degree of dependence on

natural resources, type of resources or stage of financial development. One exception is

Yuxiang and Chen (2011), who discussed the direct effect of natural resource dependence on

the financial development in Chinese provinces. However, these researchers ignored the

indirect effects of natural resources on the relationship between financial development and

economic growth. We provide the first single country evidence of the effect of oil

dependence on the financial development-economic growth relationship coupled with

examination of the channels through which oil dependence affects the finance-growth nexus.

7

Third, existing studies focus on the direct relationship between natural resource dependence

and financial development in terms of its depth or size, but largely ignore the impact on

efficiency, or the ability of the financial sector to translate savings into optimum investments

in one step, and translate this investment into economic growth in the subsequent step. Unlike

the existing literature, we pay particular attention to the role played by the efficiency of the

financial sector in understanding how the natural resource dependence might depress

economic growth through the financial development channel.

Fourth, Beck (2011) calls for more detailed treatment of how natural resource dependence

affects the financial development-economic growth relationship using better measures of

natural resource dependence. In our main results, we use oil and gas rent as a proportion of

GDP to measure oil dependence. Oil and gas rent has been the preferred measure of oil

dependence in a number of recent studies on the natural resource curse (Collier and Hoeffler,

2009; Bhattacharya and Hodler, 2010, 2014). This is a better measure of oil dependence than

used in some of the previous multi-country studies that have examined how natural resource

dependence affects the financial development-economic growth relationship, such as Nili and

Rastad (2007), who use a dummy variable to denote whether a country is oil exporting to

capture oil dependence. Compared to our approach, that measure has the limitation that it

masks differences in the degree of dependence on oil among oil exporters.

Our fifth contribution is to the debate on the role of oil dependence in the specific case of

Malaysia. While we use Malaysia to illustrate arguments likely to have application to major

oil producers more generally, in so doing specifically, we add statistical evidence to

Doraisami’s (2015) observation; based on a reading of the descriptive data, on whether

Malaysia has been successful in escaping the natural resource curse.

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The remainder of this paper is organized as follows: the literature review is presented in

section 2. In section 3, we provide an overview of the Malaysian economy. Section 4 focuses

on data and methodology, and the empirical results and discussion are presented in section 5.

Finally, section 6 concludes with policy implications.

2. Literature Review

Our study is related to four main strands of literature. The first is studies of the effect of

natural resource dependence on economic growth (Gelb, 1988; Auty, 1993; Sachs and

Warner, 1995, 2001; Sachs, 2007; Gylfason, 2001; Mehlum et al., 2006; Apergis et al., 2014;

Apergis and Payne, 2014; Betz et al., 2015; Kim and Lin, 2015). Much of the literature on the

natural resource curse is summarized in Frankel (2010). While there is wide country variation,

there is considerable empirical evidence of a natural resource curse on economic

development. Studies have shown that natural resource wealth provides the means for

autocratic andother less than democratic governments to buy off opposition and avoid

accountability and transparency. It also allows elites to hold on to power and facilitates

inefficient rent seeking that impedes economic development. This can also result in conflict,

as in some countries in Sub-Saharan Africa, that further curtails economic growth (see Collier

and Hoeffler, 2009; Beck, 2010; Bhattacharya and Hodler, 2010, 2014).

The second strand of literature to which our study is related is studies on the relationship

between financial development and economic growth. Beginning with Schumpeter (1934), a

large literature has shown that financial development can encourage economic growth by

channeling resources to highly productive investments (Patrick, 1966; McKinnon, 1973;

Shaw, 1973). These studies have shown that that financial intermediation (banks) plays an

important role in the economy by increasing saving and capital accumulation (Pagano, 1993;

9

King and Levine, 1993a, b; Levine, 1997, 2003; Hassan et al., 2011; Beck, 2011; Bittencourt,

2012).

Based on a sample of 77 countries for the period 1980–2007, Beck et al. (2014) found that, in

the long run, financial intermediation increases growth and reduces growth volatility. Both

effects have, however, become weaker over time. Law and Singh (2014) found that there is a

threshold effect in the finance-growth nexus. They found that financial development is

beneficial for economic growth up to a certain threshold; thereafter, financial development

tends to adversely affect economic growth (see also Shen and Lee, 2006; Ergungor, 2008;

Arcand et al., 2012; Cecchetti and Kharroubi, 2012; Ductor and Grechyna, 2015; Samargandi

et al., 2015)1.

The third strand of literature to which our study is related is studies of the effect of natural

resources on the financial sector and financial development. This literature includes the large

number of studies on the effect of oil prices on the financial sector, and stock market in

particular (see e.g. Narayan and Narayan, 2010; Kang et al., 2014; Demirir et al., 2015; Phan

et al., 2015). Beginning with Jones and Kaul (1996) and Sadorsky (1999), several studies

show that stock markets respond negatively to a positive oil price change; however, other

studies find no relationship (see Filis et al., 2011 for a review). Narayan and Sharma (2011)

found that the exact relationship depended on the sector in which the stock was located while

Phan et al. (2015) found that the relationship depended on whether the stock was for an oil

producer or oil consumer. Arouri and Rault (2011) found that in oil exporting countries, the

relationship between oil price shocks and stock market returns was positive. None of these

1 Refer to Ang (2009), Levine (2003) and Law and Singh (2014) for a comprehensive review on the

relationship between financial development and economic growth.

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studies, however, consider the effect of oil, or other natural resources, on the relationship

between financial development and economic growth.

The literature to which this study directly contributes, which is how natural resource

dependence affects the finance-growth nexus, is limited to a few studies. Nili and Rastad

(2007) compared the effect of financial development on economic growth for 12 oil

economies and 132 non-oil economies over the period 1992 to 2001, finding that the quality

of investment is lower in oil economies, and that this originates from the low quality of the

financial institutions, which translates into poor economic growth. For 153 countries over the

period 1980 to 2007, Beck (2011) found that countries that depend more on natural resources

tend to have an underdeveloped financial system, in which both private credit and stock

market activities are weaker, and access to credit for businesses is more limited. Finally, for

146 countries over the period 1975 to 2005, Barajas et al. (2013) found a weaker finance-

growth nexus in oil-dependent economies than in non-oil economies.

To summarize, the existing literature on how natural resource dependence affects the finance-

growth nexus consists of large cross-sectional and panel studies of several oil and non-oil

countries. There are no single country studies of the effect of oil dependence on the financial

development-economic growth relationship that also examine the channels through which oil

dependence affects the finance-growth nexus. This is a gap in the literature that we seek to

address in the current study.

3. Malaysian Economy Overview

Malaysia is the second largest oil and natural gas producer in Southeast Asia, and the second

largest exporter of liquefied natural gas globally; in addition, it is strategically located amid

11

important routes for seaborne energy trade. According to the Oil and Gas Journal, as of

January 2013, Malaysia held proven oil reserves of 4 billion barrels, the fifth-highest reserves

in Asia-Pacific after China, India, Vietnam, and Indonesia. Malaysia also has 83 trillion cubic

feet of proven natural gas reserves, and, as of January 2013, it was the third largest natural

gas reserves holder in the Asia-Pacific region. Total oil production in 2012 was estimated at

643,000 barrels per day (bbl/d).

The oil dependence indicator has witnessed fluctuations over the last 40 years caused by

fluctuations in commodity prices in the international market. The indicator was 16% in the

late 1970s, and then declined until the mid-1990s. Since 1995, the indicator rose to 18% in

2008, before the global financial crisis, which caused oil prices to decrease, and, thus, the

natural resource dependence to decrease (WDI, 2013) (see Figure 2).

[Insert Figure 2]

Since independence in 1957, Malaysia has experienced solid growth, with an average annual

growth rate of 6% over the period 1970 to 1980. GDP growth decreased significantly during

the economic recession in the mid-1980s. Then, the country recovered from the crisis in the

mid-1980s and achieved an average GDP growth rate of at least 9% from 1990 to 1996.

However, the GDP growth rate declined to -7% during the Asian financial crisis in 1997.

Since then, the economy has shown a slow process of recovery that remained inconsistent,

with an annual growth rate of 5% from 2002 to 2008. The growth rate also declined to -1.5%

in 2009 during the global financial crisis. A critical element of success is the high savings

rate that provided capital available for investment. Figure 3 presents the time series plot of

the real GDP growth rate in Malaysia for the 1970 to 2013 period.

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[Insert Figure 3]

The financial system in Malaysia can be broadly classified into financial institutions that

consist of a banking system, non-bank financial intermediaries and financial markets that are

composed of money and foreign exchange markets, capital markets, derivative markets and

offshore markets. However, because of limited development in the capital markets, the

Malaysian financial system is predominantly bank-based (Ang, 2009).

The banking system is the largest component of the financial system. The system’s growth

has been remarkable during the past few decades because of strong economic growth, a high

savings rate and advances in telecommunications. The deposits and the domestic credit to the

private sector have dramatically increased from approximately 30% and 20% of GDP in the

1970s to 130% and 135% of GDP, respectively at the beginning of the 2000s (refer to Figure

4). This growth was associated with a series of financial restructuring programs adopted by

the Malaysian authorities. Furthermore, after the Asian financial crisis, a series of

macroeconomic policy responses, such as capital controls and a reflationary policy, have

been adopted (Ang and Mckibbin, 2007).

[Insert Figure 4]

4. Data, Model and Methodology

4.1 Data and Variables

This study employs data for Malaysia over the period 1970-2013 2. All data are obtained from

the World Development Indicators (WDI). Real GDP per worker in constant 2005 USD

2 The reason for beginning and finidhing the study in 1970 and 2013 is the unavailability of data for a main

variable of interest (i.e., oil and gas rent) before and after those years.

13

prices is used as the economic growth measurement, whereas the measurement of oil

dependence, financial development, and investment are chosen based on the following

justifications.

Oil Dependence

Following Collier and Hoefler (2009) and Bhattacharya and Hodler (2010, 2014), among

others, we use oil and gas rent to GDP as the oil dependence indicator. According to the

World Bank, the oil and gas rent is defined as the difference between the value of production

for oil and gas at world prices and their total cost of production.

Financial Development

Due to limited development in the capital markets and the dominant role of the banking

system,3 we initially use one proxy to measure the level of financial intermediation.4 This

proxy is domestic credit to private sector as a share of GDP. It is considered to be one of the

best indicators to measure financial development, and has been widely used in the literature

(e.g., King and Levine, 1993a; Nili and Rastad, 2007; Shahbaz and Lean, 2012). This proxy

provides information regarding the commercial bank’s credit allocated to the private sector,

compared with the size of the economy as a whole. Therefore, this indicator accurately

3The Malaysian financial system is characterized by the limited development of the financial markets, where

banks are the main source of finance. The majority of the companies are not usually listed and the market

concentration ratio is rather high for Malaysia compared to other more advanced financial markets because

market capitalization is highly concentrated in the hands of the ten largest firms (see Ang and Mckibbin, 2007).

Thus, the Malaysian financial system can be described as a bank-based system rather than a market-based

system. Hence, the use of bank-based financial proxies is more appropriate to study the issue at hand. 4More proxies are reported for the robustness check. However, we could not include a variable for stock market

(e.g., market capitalization) due to the lack of this data, which only starts from 1981. Nevertheless, we perform

an unreported-test with the available data of market capitalization as percent of GDP and total value of stock

trade as percent of GDP. We do not find any significant impact of oil dependence on the finance-growth nexus

(result is available upon request). It can be inferred from this result that the oil curse is transmitted to economic

growth through the banking sector rather than the stock market. However, this result must be treated with

caution due to the data limitation.

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measures the role of financial intermediation in channeling funds to the private sector. The

higher ratio implies more financial services, and, therefore, greater financial development.

Investment Quantity

We use gross fixed capital formation as a share of GDP as a proxy for investment quantity.

According to the World Bank, this indicator includes land improvements (fences, ditches, and

drains) and plant, machinery, and equipment purchases; it also includes the construction of

roads, railways, schools, offices, hospitals, private residential dwellings, and commercial and

industrial buildings.

Investment Efficiency/Quality

In accordance with Dasgupta et al. (2002), investment efficiency is represented by total factor

productivity (TFP). TFP is derived from a standard neoclassical Cobb-Douglas production

function:5

1LAKY a

where Y is the real GDP, K is the real physical capital stock, and L is the total labor force.

Therefore, TFP is measured as A = TFP = y/kα, where y is the output-worker ratio (Y/L) and

k is the capital-worker ratio (K/L). Ang (2009) set the capital’s income share (α) for Malaysia

to 0.3. K is constructed using the perpetual inventory method, Kt = It + (1 - ϑ) Kt-1, where I is

real investment, and ϑ is the depreciation rate, which is assumed to be 5% in accordance with

Bosworth and Collins (2003). The initial capital stock is estimated using the Solow model

steady-state value of I0/ (ϑ+g) where I0 is the initial real investment, and g is the growth rate

in real investment over the 1970-2013 period.

5See Beck et al. (2000), Ang (2009) and Farhadi et al. (2015).

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4.2 Models

Our first objective is to identify whether oil dependence has a direct effect on economic

growth. Therefore, in the first model, we augment the neoclassical Cobb-Douglas production

function 6 by incorporating financial development and oil dependence in addition to the

capital and labor force (see Ang, 2009, Salim et al., 2014 and Lotz, 2015).

1LAKY a

where Y = aggregate GDP, L = labor, K = capital and A = TFP.

Dividing by L and taking the natural logs,

Y/L = AK(α)L(1-α-1)

Y/L = AK (α)L(-α)

Y/L = A(K/L)(α)

ln(Y/L) = ln A + αln(K/L)

Denote TFP as a function of financial development and oil dependence:

A = f (FD, OD) where FD is financial development and OD is oil dependence.

This suggests our first model:

Model (1): Y / L

t= a

0+a

1FD

t+a

2OD

t+a

3K / L

t+ e

t (1)

where Y/L is GDP per worker in constant 2005 USD prices, FD is the financial development

indicator, OD is the oil dependence indicator, K/L is capital stock per worker. 7 K is

constructed using the perpetual inventory method as described above.

For the second objective, we focus on the link between financial development and investment

quantity/quality to capture the impact of oil dependence in this relation.

6The selection of the Cobb-Douglas production function was made on the basis of its merits. The Cobb-Douglas

function is flexible and it can handle multiple inputs in its generalized form. In the presence of imperfections in

the market, it does not introduce distortions of its own; and various econometric estimation problems, like serial

correlation and heteroscedasticity, can be handled adequately and easily (Bhanumurthy, 2002). 7All variables throughout this paper are transformed into natural logarithms.

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The rationale for this argument is to determine whether oil dependence has an indirect

weakening effect on the finance-growth nexus through the investment quantity and quality

channels. Any negative effect on one of these two channels would weaken the relation

between financial development and economic growth. This argument provides important

policy implications in terms of a futuristic oil resource management policy in the country.

Inspired by Nili and Rastad (2007), the investment quantity model can be specified as follows:

Model (2): 0 1 2 3 4( * )t t t tINV FD OD FD OD REX (2)

where INV is fixed capital formation to GDP and REX is the real exchange rate of the

Malaysian Ringgit against the US dollar. Theoretically, exchange rate fluctuation has two

opposite impacts on investment. The first is a positive effect when the exchange rate

depreciates. The marginal profit of investment increases because of higher revenue from

domestic and foreign sales. However, this positive effect is counterbalanced by the rising

variable cost and the higher price for imported capital (see Harchaoui et al., 2005). Moreover,

we include REX because of its important role in natural resource-based economies (Sachs,

1999). (FD*OD) is the interaction term between oil dependence and financial development.

This interaction term is expected to reveal the impact of oil dependence on the finance-

investment nexus. At the margin, the total effect of increasing OD can be calculated by

examining the partial derivatives of investment with respect to financial development.

t

t

t ODFD

INV31

(3)

Eq. (3) indicates how the marginal effect of the financial development on investment quantity

changes with the level of oil dependence.

If the coefficient on the interaction term was negative, this implies that a small increase in oil

dependence would then result in a weaker finance-investment nexus. This would certainly be

17

the case if δ1 is positive. If, conversely, the coefficient on the interaction term is positive, a

small increase in oil dependence would result in a stronger finance-investment nexus.

Finally, an efficient financial system performs the task of screening investment projects. With

an effective risk evaluation of different investment opportunities, the selection of the most

promising investment projects could improve the quality of investment (Ang, 2009). To

assess the impact of oil dependence on the role of financial development on the

quality/efficiency of investments, we propose the following model from Beck et al. (2000):

Model (3): tttttt EDUODFDODFDTFP 43210 )*( (4)

where TFP is the natural logarithm of total factor productivity representing investment

efficiency. TFP is calculated as described in Section 4.1. EDU is the secondary school

enrolment (% gross). EDU is a proxy for human capital, which is considered to be a main

determinant for TFP (Benhabib and Spiegel, 1994; Anwar and Sun, 2011). The interaction

term between financial development and oil dependence captures the effect of oil dependence

on the role of financial development in investment efficiency.

t

t

t ODFD

TFP31

(5)

In equation (4), the coefficient φ3 represents the effect of oil dependence in the relation

between financial development and investment efficiency. If φ3 < 0, oil dependence has a

negative effect on the role of financial development in boosting investment efficiency; this

implies a weaker finance-growth nexus. However, if φ3 > 0, oil dependence has a positive

effect on the role of financial development in boosting investment; this implies a stronger

finance-growth nexus. Eq. (5) indicates how the marginal effect of the financial development

on TFP changes with the level of oil dependence. The sign on φ3 shows whether oil

dependence has a positive or a negative effect on the role of financial development on TFP.

18

4.3 Methodology

To test the long-run relationships among the variables, we adopt the auto-regressive

distributed lag (ARDL) bounds testing approach to cointegration suggested by Pesaran et al.

(2001). Most recent studies indicate that an ARDL model is preferable for estimating the

cointegration relation because it is applicable irrespective of whether the underlying

regressors are I(0) or I(1), and it performs well for a small sample size. In addition, one of the

important statistical advantages of the ARDL approach for the cointegration test is that, as

Pesaran and Shin (1998) note, appropriate modification of the orders of the ARDL model is

adequate to correct the problem of endogeneity bias (see also Ang, 2009).

The ARDL version of the estimation models can be specified as:

0 1 2 1 3 1 4 5 6

1 01 1

7 8

0 0

t i

t i

pm

t t

i it t t t i

q r

t

i i t i

Y Y K YFD OD FD

L L L L

KOD

L

1 1 1 110 1 2 3 4 5 6

1

7 8 9 10

0 0 0 0

( * )

( * )

t t t tt

t i t it i

m

t t i

i

p q r s

tt i

i i i i

INV INV FD OD FD OD REX INV

FD OD FD OD REX

1 1 1 110 1 2 3 4 5 6

1

7 8 9 10

0 0 0 0

( * )

( * )

t t t tt

t i t it i

m

t t i

i

p q r s

tt i

i i i i

TFP TFP FD OD FD OD EDU TFP

FD OD FD OD EDU

where m,p,q,r and s are the optimal lag lengths for each variable.

The coefficients (α1, α2, α3, α4), ( 54321 ,,,, ) and (δ1, δ2, δ3, δ4, δ5) of the first portion of

the models measure the long-run relations, whereas the coefficients (α5, α6, α7, α8)

( 109876 ,,,, ) and (δ6, δ7, δ8, δ9, δ10) represent the short-run dynamics. The F-statistic is

19

used to test the existence of a long-run relation among the variables. We test the null

hypotheses, H0: α1 = α2 = α3 = α4 = 0; 054321:0 H ; H0: δ1= δ2= δ3= δ4= δ5

= 0 that there is no cointegration among the variables. The F-statistics are then compared

with the critical values provided by Narayan (2005), which are appropriate for small samples.

If the computed F-statistic is greater than the upper bound critical value, we reject the null

hypothesis of no cointegration and conclude that a steady-state equilibrium exists among the

variables. If the computed F-statistic is less than the lower bound critical value, the null

hypothesis of no cointegration cannot be rejected. However, if the computed F-statistic lies

between the critical values for the lower and upper bounds, the result is inconclusive. We

then adopt the modified version of the Granger causality test developed by Toda and

Yamamoto (1995), and Dolado and Lutkepohl (1996), hereafter (TYDL).

5. Empirical Findings and Discussion

Although the ARDL model does not require pre-testing variables, we need to ensure that that

none of the variables are integrated in order 2 or beyond. Because of its reliability for a small

sample size, the Dicky-Fuller GLS stationary test is employed to examine the time series

properties for each variable and determine its order of integration. Table 1 reveals that all the

variables are integrated in order one with the exception of OD and INV, which are I(0).

Hence, it is confirmed that the ARDL approach can be applied to analyze the long-run

relationship. To model the structural breaks, we implement the Narayan and Popp (2010) M1

and M2 two-break unit root test and the result is presented in Table 2. Both Narayan and

Popp (2010) M1 and M2 unit root tests suggest the same break dates. We incorporate the

structural breaks in the cointegration test and model estimation.

[Insert Table 1]

20

[Insert Table 2]

After investigating the time series properties for all variables, the ARDL approach is used to

examine the potential long-run equilibrium relation. This test is sensitive to the number of

lags used. Given the limited number of observations in this study, lags with a maximum of

two years have been imposed on the first difference of each variable, and the Schwarz-

Bayesian Criterion (SBC) is used to select the optimal lag length for each variable. The

results of the ARDL bound test of cointegration are tabulated in Table 3. In this table, we

divide the results into two parts. In the first part, we do the ARDL test without structural

break. In the second part, we repeat the test after adding dummies for the break points

suggested by the Narayan and Popp (2010) unit root test. In general, we do not find any

significant differences for both sets of ARDL results.

[Insert Table 3]

Table 3 shows that the calculated F statistic is higher than the upper bound critical value at 5%

in our third model. This result provides strong evidence for the existence of a long-run

relation among the variables in these two models. However, we fail to prove the existence of

a long-run relation through the F-statistic in our first and second models. According to

Kremers et al. (1992), the significant coefficient of the lagged error-correction term (ECT) is

an alternative, and more efficient, means of establishing cointegration. Our results show that

the coefficient on the lagged ECT is negative and significant in all models. This finding

confirms the existence of a long-run relation in all models.

21

Because there is cointegration among the variables, we can derive the long-run coefficient as

the estimated coefficient of the one lagged level independent variable divided by the

estimated coefficient of the one lagged level dependent variable and multiply it with a

negative sign. Conversely, the short-run coefficients are calculated as the sum of the lagged

coefficients of the first differenced variables.

[Insert Table 4]

Table 4 Panel A reveals that the level of financial development has a direct negative impact

on economic growth in the long-run. The conclusion from this result is that an increase in

credit to the private sector does not per se contribute to growth. Most important is how this

credit is used and how effectively it is allocated to finance growth-enhancing projects. Ang

and Mckibbin (2007) argued that the financial sector in Malaysia has not been playing a vital

role in allocating resources. Most of the credit provided to the private sector was issued for

the purchase of shares and real estate property, rather than for investment in productive

activities. This led to bubbles in the property sector and triggered many speculative activities

in the share market prior to the financial crisis in 1997–98. This risky behavior resulted in the

mismanagement of assets and generated many larger non-performing loans.

Nevertheless, the estimated long-run coefficient shows that oil dependence is not significant.

The absence of a significant negative impact of oil dependence on economic growth is not

surprising and supports the previous finding (Gylfason, 2001; Rosser, 2006) that Malaysia

has escaped the resource curse. Conversely, we find that capital has played a crucial role in

shaping Malaysia’s economic growth.

22

Because we could not find direct evidence of an oil curse from Model 1, we attempt to seek

an indirect symptom of the oil curse from Model 2. The empirical results pertaining to Model

2 are presented in columns 2 and 5 of Panel A in Table 4. Financial development and oil

dependence have positive and significant impacts on investment in the long run. However,

this positive impact decreases by increasing the degree of oil dependence, as indicated by the

negative sign on the interaction term between financial development and oil dependence.

Therefore, we cannot assert that Malaysia has fully escaped the oil curse because we have

found a symptom of the oil curse in the financial sector. Sachs (2007) argued that one reason

for the oil curse is that the high dependence on oil adversely affects other economic sectors,

particularly the sectors that can drive sustainable economic growth.

The result of Model 3 in columns 3 and 6 corroborates our previous argument that financial

development does not affect TFP in the long run. Moreover, the coefficients of oil

dependence and the interaction term between financial development and oil dependence are

not significant. This result suggests that the financial sector in Malaysia may not be

sufficiently developed to foster the efficiency of investment. These results expose the illusory

role of oil revenue in Malaysia’s economy. Although oil revenue does not have a significant

impact on economic growth in the long run, it would negatively affect the mechanisms of

certain growth determinants in the long-run, such as the finance-investment nexus found in

our long-run analysis

Table 4 Panel B shows the short-run results. The results from Model 1 reveal that oil

dependence has a positive, but insignificant, impact on economic growth in the short-run.

Similar to our long-run results, financial development has a negative effect and capital stock

has a positive impact on economic growth in the short run. The illusory role of oil

23

dependence is also reflected in the absence of a significant negative effect of the interaction

term on investment in columns 2 and 5. These results for the short-run can be attributed to the

fact that the oil curse symptoms most likely appear in the long-run as per the findings of

Torvik (2001), and Collier and Godris (2008).

Finally, the coefficients for the estimated lagged error correction term are negative and

significant in all models. This confirms the existence of a long-run relation among the

variables. In addition, the coefficient suggests that a deviation from the long-run equilibrium

following a shock is corrected by approximately 52%, 42% and 8% per year, respectively.

Panel C in the same table notes that all models pass all the diagnostic tests for serial

correlation, autoregressive conditional heteroskedasticity and model specification. 8 The

CUSUMSQ in Figures 5 to 10 remain within the critical boundaries for the 5% significance

level. These statistics confirm that the long-run coefficients and all short-run coefficients in

the error correction model are stable.

[Insert Figures 5-10]

Theoretically, financial intermediation affects economic growth mainly by mobilizing

savings and allocating funds to productive investment projects that will generate strong

returns. Because financial development does not have a positive effect on economic growth,

as reflected in Model 1, one may conclude that, although the financial sector channels funds

to investment, the funds are not efficiently placed into productive investments that will

expand the economy. The reason for this distortion may be the high dependence on oil rent in

Malaysia. The high dependence on oil rent increases the dominant role of government in total

8We did not find any evidence of a multicollinearity problem among our independent variables, except for

the interaction term between financial development and oil dependence. However, multicollinearity

associated with the interaction can be considered as one of the cases where we can safely ignore

multicollinearity. This is because the effect of multicollinearity is mainly on the coefficients of the

variables, and there is no effect on the p-value or the relationships in the models (see, Allison, 2012).

24

investment at the expense of private investment (Nili and Rastad, 2007). Because private

investment is more efficient than public investment, the high dependence on oil rent replaces

more efficient investment with less efficient investment, and thus, growth suffers (Banerjee,

2011). Therefore, we argue that the oil curse in Malaysia lies in crippling the mechanism of

finance-growth, i.e., investment, as shown in Model 2. The oil curse is transmitted through

the quantitative channel rather than the qualitative channel.

The ability of financial intermediaries to accumulate capital may be impaired because of the

uncertainty that stems from the volatile nature of oil prices. Subsequently, this uncertainty

weakens the incentive of banks to invest and encourages banks to pursue lower risk activities,

such as consumption or housing loans.9 The impact on the level of capital, rather than the

productivity of capital, may be the reason that oil dependence biases the finance-growth

nexus in Malaysia. These findings are in accord with the fact that, until the early 1970s, the

bulk of financial sector credits were unproductive loans that were channeled into inefficient

activities rather than into high quality investments (refer to Ghani and Suri, 1999; Hill, 2005).

Finally, the TYDL causality tests in Table 5 reveal that there is unidirectional causality

running from capital stock to economic growth. We also find that the interaction term

between financial development and oil dependence causes investment. This result supports

our argument that the interactive relationship between financial development and oil

dependence affects the level of investment in Malaysia.

(Insert Table 5)

5.1 Robustness Check

As a robustness check, we re-estimate our models using several alternative proxies for

financial development and also a new oil dependence proxy. In addition, we conduct these

9Thiel (2001) argued that, in some countries, significant numbers of bank loans are provided to finance housing

loans, instead of being channelled to finance productive investments.

25

estimations utilizing two other econometric approaches, which are FMOLS and DOLS. In

general, the outcomes of our robustness check support our previous findings with the ARDL

approach.

Our alternative financial development proxies are LIQ, which equals liquid liabilities divided

by GDP. Many researchers have used this variable as a measure of financial depth. LIQ does

not represent the effectiveness of the financial system. However, by assuming that the size of

the financial intermediary system is positively correlated with the financial system’s activities,

in our regressions, this can be used as a measure of financial development. The second proxy

used is deposits to GDP (DPS), which equals the demand, time and saving deposits in deposit

banks and other financial institutions as a share of GDP. The size of deposits is an indicator

for the potential investment quantum. The third proxy is the PCA, which is a new proxy

constructed by the previous three based on the Principal Components Analysis approach.

This proxy is able to capture most of the information from the original dataset, which consists

of the previous three financial development measures.

Additionally, we use an alternative proxy for oil dependence. This proxy is a dummy variable

for the number of years where the oil rent is greater than 10% of GDP (see Beck, 2011). The

results of our robustness check, which are tabulated in Table 6, are in line and in agreement

with our main models that confirm our stated argument and conclusion.

[Insert Table 6]

6. Conclusion and policy implications

This paper empirically examines the oil curse in Malaysia and the role of investment as a

mechanism of the oil curse. We do not find any significant evidence of the direct effect of

financial development on economic growth and TFP. However, there is a direct and positive

26

effect of financial development and oil dependence, respectively, on the level of investment.

Furthermore, a significant negative interaction term between financial development and oil

dependence exposes the oil curse in Malaysia. This finding supports Doraisami (2005, p. 107),

who stated, “Malaysia has certainly not been untouched by the [oil] curse”.

Nevertheless, given the absence of a significant impact of financial development on TFP, we

argue that the financial sector in Malaysia channels funds inefficiently into non-productive

investment. The reason for this distortion of financial development may be the high

dependence on oil rent in Malaysia. Oil dependence weakens and cripples the mechanism of

one of the most important economic growth motors, which is financial development. We

argue that the oil curse in Malaysia exists and that the curse is transmitted through the

quantitative channel rather than the qualitative channel.

Our findings contain a mix of policy implications for Malaysia. On the one hand, the

Malaysian government should be aware of the indirect risk of oil dependence on the financial

role in fostering investment activities. It is advisable to maintain the degree of oil dependence

at a low level, enhance economic diversification activities and increase the contribution of

other sectors to GDP. Additionally, the financial sector should be more involved in

productive investment activities to enhance its role in fostering economic growth. In this

regard, policymakers should pursue actions that enhance the efficiency of bank

intermediation. Introducing improvements in information on prospective borrowers,

including the establishment of credit bureaus and enhancing the legal protection of creditor

rights, are all potential areas in which quality gains can be achieved.

27

Furthermore, one of the main oil curse channels in oil-dependent economies is

mismanagement and neglect of human development. The easy access of oil rent may relieve

the government from developing its human capital, which may negatively affect the

performance of various economic sectors including the financial sector. Therefore, we

suggest that policymakers need to work to enhance human development, which has an

important positive role in investment efficiency.

28

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35

Table 1 Results of Unit Root Test

Variable DF-GLS

Level 1st Difference

Y/L 0.3196 -5.6460***

K/L -2.5275 -3.4565**

FD -0.2860 -2.1820**

OD -0.9592*** -0.2789***

INV -1.8223 -4.2180***

REX -0.7042 -5.6012***

TFP 0.8333 -5.7807***

EDU -0.6735 -5.0945***

Note: *** and* denote significance at the 1% and 10% levels, respectively.

Table 2: Results of Narayan-Popp (2010) two breaks unit root test

M1

M2

Series

t-stat k

t-stat k TB1 TB2

Y/L

-0.1791 0

-3.1890 0 1984 1997

INV

-3.8367 1

-3.0818 1 1997 2000

TFP

-1.6532 0

-3.4280 1 1984 1997

Notes: M1 is Narayan and Popp’s Model 1. M2 is Narayan and Popp’s Model 2. TB is the structural break and K is the lag length.

36

Table 3: Result from ARDL Cointegration Test

Without Structural Break With Structural break

Equation Model (1) Model (2) Model (3) Model (1) Model (2) Model (3)

Optimal lag (1,0,0,1) (2,0,0,0,1) (1,0,0,1,1) (1,0,0,1) (2,0,0,0,1) (1,0,0,1,1)

F-statistic 2.7813 1.7688 5.0708** 2.6255 1.7998 4.9206**

ECt-1 -0.5177*** -0. 4152*** -0.0768*** -0.4600*** -0.3251*** -0.0687***

Critical Values K=3 K=4

1% level 5 % level 10% level 1% level 5 % level 10% level

Lower Bound 4.983 3.535 2.893 4.394 3.178 2.638

Upper Bound 6.423 4.733 3.983 5.914 4.450 3.772

Note: *** and ** denotes the significance at 1% and 5% level respectively. Critical values bounds are from Narayan (2005) with unrestricted intercept and no trend.

37

Table 4: Long Run and Short Run Analysis

Panel A. Long Run Results

Without Structural Breaks With Structural Breaks

Model (1) Model (2) Model (3) Model (1) Model (2) Model (3)

Dependent

variable Y/L INV TFP Y/L INV TFP

C 2.1177*** (9.9486)

-2.4778

(-1.3379) -0.2499

(-0.0792) 2.1328*** (9.8071)

-2.3697 (-1.4067)

-1.5035 (-0.4068)

FD -0.2654***

(-6.7480)

0.8077***

(3.0016) 0.1508

(0.4668)

-2.2693***

(-6.6144)

0.8073***

(3.2987)

0.1455

(0.4149)

OD 0.0004

(0.0294)

1.1369**

(2.1040) 0.3844

(0.5581)

0.0018

(0.1264)

1.2550**

(2.5556)

0.5137

(0.6887)

K/L 0.8271***

(23.3740) - -

0.82711***

(22.9249) - -

FD*OD - -0.3135**

(-2.2217) -0.1371

(-0.7068) -

-0.3400**

(-2.6575)

-0.1829

(-0.8502)

REX - 0.5785*

(1.9773) - -

0.5519**

(2.0995) -

EDU - - 1.6213* (1.9895)

- - 1.9783** (2.0481)

DUM1984 - - - 0.0025

(0.0475) -

0.5145

(1.0904)

DUM1997 - - - 0.0333

(0.5890)

0.3505 (1.3291)

0.1964

(0.5473)

DUM2000 - 0.5484*

(1.7630) -

Panel B. Short Run Results

ΔFD -0.1185***

(-3.1257)

0.1918

(0.9893) -0.0034

(-0.0902)

-0.1147***

(-3.2210)

0.2870

(1.5773)

-0.0036

(-0.0972)

ΔOD 0.0014

(0.1309)

0.0897

(0.2931) -0.0053

(-0.1053)

0.0025

(0.2550)

0.4320

(1.4764)

-0.0043

(-0.0920)

38

ΔK/L 0.9025***

(9.3539) - -

0.8299***

(9.3027) - -

Δ(FD*OD) - -0.0305

(-0.3835) 0.0054

(0.3880) -

-0.1267

(-1.6236)

0.0049

(0.3740)

ΔREX - 0.6799***

(3.2475) - -

0.5566***

(2.8558) -

ΔEDU - - -0.4718***

(-4.6242) - -

-0.4479***

(-3.9403)

DUM1984 - - - 0.0257

(1.6210) -

0.0631***

(4.0656)

DUM1997 - - - 0.0529***

(3.3562)

0.2585***

(4.0819)

0.0272

(1.5481)

DUM2000 - - - - 0.2677***

(3.5135) -

ECt-1

-0.5177***

[-4.2334]

-0.4152***

(-3.4667)

-0.0768***

(-5.7015)

-0.4600***

(-3.9151)

-0.3251***

(-3.3544)

-0.0687***

(-5.6848)

Panel C Diagnostic Tests

Test F-Statistic F-Statistic F-Statistic F-Statistic F-Statistic F-Statistic

LM 1.5320

[0.2303]

1.4838

[0.2420]

0.4305

[0.6542]

1.7852

[0.1836]

0.4716

[0.6286]

0.5331

[0.5926]

ARCH 0.4669

[0.6305]

0.8540

[0.4339]

0.8089

[0.4540]

0.5949

[0.5567]

1.7985

[0.1797]

1.3011

[0.2858]

RESET 0.6430

[0.4279]

1.6115

[0.2145]

0.2531

[0.6184]

1.0618

[0.3101]

1.3202

[0.2812]

0.4622

[0.5020]

Note: ***, **, * denotes the significance at 1%, 5% and 10% levels respectively. t-statistic is in the parenthesis, P-

value is in the brackets.

39

Table 5: Results of TYDL Causality Tests

χ2 Y/L→ FD 0.4277

FD→Y/L 3.7904

K/L→Y/L 8.7203**

INV→FD 1.5086

FD→INV 0.8429

(FD*OD)→INV 5.0655*

FD→TFP 0.1985

TFP→FD 3.5056

40

Table 6: Robustness Check

Model 1 Model 2 Model 3

FMOLS DOLS FMOLS DOLS FMOLS DOLS

FD proxy LIQ DPS PCA LIQ DPS PCA LIQ DPS PCA LIQ DPS PCA LIQ DPS PCA LIQ DPS PCA

Constant 3.282*** 3.134*** 1.807*** 3.412*** 3.229*** 1.642*** -0.528 -0.092 0.753 -0.198 0.193 1.459 1.468*** 1.262** 0.105 0.418 0.421 -0.977

(18.339) (16.437) (5.626) (14.193) (13.018) (4.480) (-0.277) (-0.050) (0.557) (-0.088) (0.091) (0.818) (2.777) (2.176) (0.091) (0.560) (0.516) (-0.795)

FD -0.246*** -0.209*** -0.106*** -0.307*** -0.270** -0.121*** 0.373** 0.311* 0.146** 0.378* 0.314* 0.132* -0.307* -0.283** -0.096* -0.185 -0.175 -0.067

(-3.592) (-3.300) (-5.019) (-3.513) (-3.259) (-4.967) (2.059) (1.936) (2.499) (1.828) (1.733) (1.849) (-2.027) (-2.046) (-1.705) (-0.887) (-0.832) (-1.086)

ODd -0.004 -0.0002 -0.002 0.018 0.027 0.016 3.915*** 3.399*** 0.078 5.794*** 4.869*** 0.114 0.066 0.033 -0.060 1.448 1.113 0.023

(-0.188) (-0.011) (-0.113) (0.582) (0.773) (0.677) (2.746) (2.725) (0.622) (2.926) (2.881) (0.571) (0.098) (0.055) (-0.960) (1.411) (1.144) (0.307)

K/L 0.708*** 0.703*** 0.751*** 0.723*** 0.721*** 0.768*** - - - - - - - - - - - -

(19.607) (18.661) (22.201) (17.536) (16.510) (20.374)

FD*ODd - - - - - - -0.882*** -0.787*** -0.277** -1.319*** -1.143** -0.457*** -0.028 -0.020 -0.017 -0.327 -0.258 -0.121*

(-2.852) (-2.852) (-2.601) (-3.127) (-3.130) (-2.935) (-0.193) (-0.150) (-0.312) (-1.472) (-1.202) (-1.731)

REX - - - - - - 0.455* 0.431 0.519* 0.405 0.393 0.398 - - - - - -

(1.734) (1.634) (1.912) (1.292) (1.267) (1.097)

EDU - - - - - - - - - - - - 1.529*** 1.544*** 1.538*** 1.647*** 1.629*** 1.798***

(6.169) (6.027) (5.164) (5.762) (5.119) (5.717)

Note: ***, **, * denotes the significance at 1%, 5% and 10% levels respectively. t-statistic is in the parenthesis.

41

Figures

Investment

Fig 1. Oil Dependence Transmission Mechanism into Finance-Growth Nexus

0

4

8

12

16

20

1970 1975 1980 1985 1990 1995 2000 2005 2010

Fig.2. Oil and Gas Rent to GDP (%) Source: WDI

Financial

Development

Economic

Growth

Quantitative

Qualitative

Financial

Development

Quantitative

Qualitative

Financial

Development

Quantitative

Qualitative

Oil Dependence

Economic

Growth Financial

Development

Quantitative

Qualitative

42

-8

-4

0

4

8

12

1970 1975 1980 1985 1990 1995 2000 2005 2010

Fig.3. Annual Growth Rate of GDP (%) Source: WDI

0

20

40

60

80

100

120

140

160

1970 1975 1980 1985 1990 1995 2000 2005 2010

Fig.4. Domestic Credit to Private Sector to GDP (%)

Sources: WDI

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1980 1985 1990 1995 2000 2005 2010

CUSUM of Squares 5% Significance

Fig. 5. Plot of cumulative sum of squares of recursive

residual (without structural break) (Model 1)

Fig. 6. Plot of cumulative sum of squares of recursive

residuals (with structural break) (Model 1)

-0.4

0.0

0.4

0.8

1.2

1.6

99 00 01 02 03 04 05 06 07 08 09 10 11 12 13

CUSUM of Squares 5% Significance

43

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1980 1985 1990 1995 2000 2005 2010

CUSUM of Squares 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

01 02 03 04 05 06 07 08 09 10 11 12 13

CUSUM of Squares 5% Significance

Fig. 7. Plot of cumulative sum of squares of recursive

residual (without structural break) (Model 2)

Fig. 8. Plot of cumulative sum of squares of recursive

residuals (with structural break) (Model 2)

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

CUSUM of Squares 5% Significance

Fig. 9. Plot of cumulative sum of squares of recursive

residual (without structural break) (Model 3).

Fig. 10. Plot of cumulative sum of squares of recursive

residuals (with structural break) (Model 3).