8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
1/24
I. INTRODUCTION
The demand for money function creates a background to review the effectiveness of
monetary policies as an important issue in terms of overall macroeconomic stability. Money
demand is an important indicator of growth of a particular economy. An increasing money
demand mostly indicates improvement in a countrys economic situation as opposed to
falling money demand which normally indicates a deteriorating economic climate. This
results from the fact that a rising money demand brings about an increase in production that
causes the rate of money circulation to decline while a falling money demand results in
restricted production that causes the rate of money circulation to increase.
There are short-term and long-term aspects of money demand. The long-term aspect of
money demand or the need for money relates to growing production. This means that the
increased issue of money, which is consistent with price stability, may solely be achieved in
the long run if it follows the growth of output. In the short-term, a decreasing rate of money
circulation may cause the money demand to rise regardless of the movements in production.
However, continuous increase in money supply, irrespective of trends in production, leads to
stronger inflationary pressures.
In developed countries, implementations of monetary policy changes were used to alter
short-run business cycle fluctuations, although long-run price movement was likewise, the
more important objective. In developing countries like the Philippines, however, long-run
economic growth were a major focus of monetary policy, where money expansion is
frequently used as a major source of the governments demand management.
Theoretically, demand for real money balances could be divided into transactions demand
component, which is positively related to income and inversely related to interest rates,
precautionary demand component, which is positively related to income, and speculative
demand component, which is inversely related to interest rates. In developing countries like
the Philippines, using broad money (M2) is very much prevalent. Moreover, the government,
businesses and investors are using credit or lending to ensure the smooth running of their
1
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
2/24
development activities. The banking system and other financial institutions create money by
giving loans. In addition, it is a practice that during economic boom and the returns on
investment is high, banks and other financial institutions employ a relatively lower cost of
credit (i.e. interest rate) to stimulate borrowing. By contrast, during economic crisis, either by
inflation or deflation, the banks and other financial institutions increase the cost of credit in
order to discourage the clients from borrowing. Therefore, an increase in the cost of
borrowing is likely to decrease the demand for money.
The objective of this paper is to empirically investigate whether an equilibrium
relationship exists between certain combinations of money balances, real national income, an
opportunity cost measure, and price level. This study attempts to determine factors affecting
the demand for money in the Philippines. Furthermore, this paper examines the role of
interest rates in the money demand function as the appropriate measure of opportunities cost
of holding money.
Understanding public demand for newly created money is important because it has
several implications on critical macroeconomic variables such as income, interest rates,
expected inflation, and exchange rates. Nevertheless, money demand plays a vital role in the
success or failure of a countrys development. Thus, knowledge regarding money demand
and the factors affecting it is a must for government policymakers, businessmen, investors
and the like.
Review of Related Literature
There were a number of studies that examined the relationship between certain
combinations of money balances, real national income, an opportunity cost measure, and
price level.
Hossain (1988) estimated a short-run money demand model for Bangladesh using
quarterly data from 1974:1 to 1985:4. The author found a Laidler (1982) short-run real
money demand model, which is appropriate for Bangladesh on the basis of the set of criteria
2
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
3/24
suggested by McAleer et al. (1985). On the basis of MacKinnon et al. (1983) non-nested test
of model selection, the author concluded that neither the log-linear nor the linear functional
form has any advantage over the other for Bangladesh. The author found the real permanent
income and expected inflation rate are the significant explanatory variables in the demand for
money function. The real permanent income was measured as four quarters unweighted
moving average of actual real income and expected inflation was measured as one-period
lagged inflation rate. Finally he found that both narrow money (M1) and broad money (M2)
functions were empirically stable.
Bahmani-Oskooee and Rehman (2005) analyzed the money demand functions for India
and six other Asian countries during the period beginning with the first quarter of 1972 and
ending with the fourth quarter of 2000. Using the ARDL approach described in Pesaran et al.
(2001), they performed cointegration tests on real money supplies, industrial production,
inflation rates, and exchange rates (in terms of US dollar). For India, cointegrating
relationships were detected when money supply was defined as M1, but not M2, so they
concluded that M1 is the appropriate money supply definition to use in setting monetary
policy.
Contrasting with the above, there is also prior research that uses money supply defined
broadly in holding that India's money demand function is stable. In one example, Pradhan
and Subramanian (1997) employed cointegration tests, an error correction model, and annual
data for the period of 1960 to 1994 to detect relationships among real money balances, real
GDP, and nominal interest rates. They estimated an error correction model using M1 and M3
as money supply definitions and found the error correction term to be significant and
negative. Their position, therefore, was that the money demand function is stable not only
with M1 but also with M3.
The early versions of the quantity theory of money (mainly Fishers, 1911, equation of
exchange, and the Cambridge approach, e.g. Pigou, 1917) emphasized the proportionate
relationship between the amount of money in circulation, the volume of transactions, and the
price level.
3
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
4/24
Nevertheless, Nelson (2005) made a study regarding the relationship between U.S.
Treasury bond yield and M1 per dollar of GDP and had the following results:
Both figures do form a pattern that has the general shape of the demand function. They
are downward sloping and concave, flattening as they approach the X axis and steepening as
they approach the Y axis. Their points do not, however, lie exactly along a smooth line,
rather they appear to be scattered around a curve that has the general shape of the demand
function.
In contrast to the aforementioned studies, this paper uses time series data from the
Philippines. It also focuses on the four macroeconomic variables namely real money supply
(specifically narrow [M1] and broad [M2] money supply), real national income, interest rate,
and price level. It also differs from the aforementioned studies in that it uses the most recent
4
Scatter Plot of T-Bond Yieldand M1 per dollar of GDP
Scatter Plot of T-Bill Yieldand M1 per Dollar of GDP
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
5/24
data. While the majority of previous researchers use data from the 1970s to 1980s, I use
data from the 2000s.
II. CONCEPTUAL FRAMEWORK
The demand for money theory, also known as liquidity preference, deals with the desire
to hold money rather than other forms of wealth (e.g. stocks and shares). It is particularly
associated with the work of English economist John Maynard Keynes. Keynes distinguished
three motives for holding money: the transaction motive, the speculative motive, and the
precautionary motive. The transactions motive is money used for the purchase of goods and
services. The transactions demand for money is positively related to real incomes and
inflation. As an individual's income rises or as prices in the shops increase, he will have to
hold more cash to carry out his everyday transactions. The quantity of nominal money
demand is therefore proportional to the price level in the economy. The speculative motive ismoney not held for transaction purposes but in place of other financial assets, usually
because they are expected to fall in price. The precautionary motive is money held to cover
unexpected items of expenditure. Like the transactions demand for money, precautionary
demand for money is positively correlated with real incomes and inflation.
Keynes demonstrated that there was an inverse relationship between the price of a bond
and the rate of interest. Conversely, if the rate of interest increases, the price of bonds will
fall.
There is an inverse relationship between interest rates and the market prices of fixed
interest government securities.
Keynes argued that each individual has a view about an 'average' rate of interest. If the
current interest rate was above the average rate then a rational individual would expect
interest rates to fall. Similarly, if current rates are below the average rate then obviously
interest rates would be expected to rise.
5
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
6/24
At high rates of interest, individuals expect interest rates to fall and bond prices to rise.
To benefit from the rise in bond prices individuals use their speculative balances to buy
bonds. Thus when interest rates are high speculative money balances are low.
At low rates of interest, individuals expect interest rates to rise and bond prices to fall. To
avoid the capital losses associated with a fall in the price of bonds, individuals will sell their
bonds and add to their speculative cash balances. Thus, when interest rates are low
speculative money balances will be high. Consequently, there is an inverse relationship
between the rate of interest and the speculative demand for money.
The total demand for money is obtained by summating the transactions, precautionary
and speculative demands. Represented graphically, it is sometimes called the liquidity
preference curve and is inversely related to the rate of interest.
The Demand for Money and the Rate of Interest
During periods of sustained economic growth, rising real incomes and increasing
numbers of people employed, demand for money at each rate of interest tends to increase.
6
Interest
Rate (r)
9%
7%
5%
Real Money Demand
Money
Demand
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
7/24
Therefore higher real national income causes an outward shift in the demand for money. This
is shown in the diagram below:
Money Demand and Increases in Real GDP
7
Interest
Rate (r)
9%
7%
5%
Real Money Demand
MD1
MD2
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
8/24
The general approach I will be using in analyzing my data is as follows:
8
Formulate an econometric
model and choose the type of
functional form to use.
Distinguish the dependent or
explained variable from the
independent or explanatory
variable/s.
Determine the appropriatestatistics/data that best represent
variables.
Determine whether to use
Ordinary Least Squares (OLS)
or Generalized Least Squares
(GLS) estimation.
Run the regression.
Interpret coefficients.
Conduct the tests of hypothesis
for the coefficients.
Interpret the coefficient of
determination, R2, and the
adjusted coefficient of
determination, R2.
Check for normality of error
terms.
Detect for signs ofmulticollinearity,
heteroskedasticity, and serial
correlation.
If there are signs of the
aforementioned problems inmultiple regression, diagnose.
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
9/24
III. ECONOMETRIC MODEL AND ESTIMATION PROCEDURE
a.Econometric Model
ln(Mt) = 1 + 2ln(Yt) + 3Rt + 4ln(Pt) + t
where Mt = real quantity of money
Yt = real national income
Rt = interest rate
Pt = price level
Variables
Real Quantity of Money (Mt) refers to the quantity of money available in the
Philippine economy. In this study, data on the broad money (M2) of the Philippines
was used.
Real National Income (Yt) refers to the Gross Domestic Product (GDP) of the
Philippines. GDP is the market value of all final goods and services produced within
a country in a given period of time.
Interest Rate (Rt), in this model, is quantified through data on 91-day Philippine
Treasury Bills. Treasury Bills (T-Bills)are government securities which mature in
less than a year. There are three tenors of T-Bills: (1) 91 day (2) 182-day (3) 364-day
maturities. The number of days is based on the universal practice around the world of
ensuring that the bills mature on a business day. T-Bills are quoted either by their
yield rate, which is the discount, or by their price based on 100 points per unit. Thosethat mature in less than 91 days are called Cash Management Bills (e.g. 35-day, 42-
day). T-Bills do not bear interest but are rather issued and sold at a discount from face
value (they cant be traded at a premium) and are redeemed at maturity for the full
face value of the instrument.
9
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
10/24
Price Level (Pt), in this model, is quantified through data on Consumer Price
Index (CPI) which is a measure of the overall cost of the goods and services bought
by a typical consumer.
Functional Form
The model assumes a log-linear form in real quantity of money (M t), price level
(Pt), and real national income (Y t). Meanwhile, it assumes a linear form in interest rates (Rt).
The aforementioned functional forms were employed based on Keynes theoretical
assumptions on the demand for money. Essentially, he made the transactions and
precautionary balances functions of the level of income, and speculative balances a functionof the current rate of interest and the level of wealth.
Under Keyness assumptions, the demand for money, where W represents wealth, can be
written as:
MD =[kY + l(r) W] P
In the equation, kYrepresents transactions and precautionary balances, and
l(r)Wrepresents speculative balances (l), which are a function of the interest rate.
Traditionally, the standard theory of the demand for money has been tested empirically
by estimating the equation:
MD = (P, Y, R)
where MD is expected to be a stable function of a small number of key macroeconomic
variables which includes P, the price level; Y, a scale variable (income); and R, a vector of
interest rates, representing the opportunity cost of holding money.
10
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
11/24
Price homogeneity is frequently imposed, which is a testable restriction, given that the
units of a currency are irrelevant.
So the equation becomes:
MD =f(Y.R)
P
Taking logarithms of the equation yields (hereafter small caps represent logs of variables):
ln(M) = 1 + 2ln(Y) + 3R + 4ln(P) +
Hence, the equation assumes log-linearity in money, prices, and income, and linearity in
interest rates, which is a common functional form.
b. Estimation Procedure
I used the Ordinary Least Squares (OLS) method in estimating the parameters of my
econometric model. I chose the OLS estimation procedure over the General Least Squares
(GLS) method because the former is consistent when regressors (real national income,
interest rates, and price level in this case) are exogenous and there exists no problem of
multicollinearity. In addition, OLS can be derived as a maximum likelihood estimator under
the assumption that error terms t are normally distributed.
11
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
12/24
IV. THE DATA
Variable Descriptions
m2r real money supply, 2000-2010
gdpr real gross domestic product, 2000-2010
tbr3 interest rate on three-month (91-day) treasury bills
p consumer price index, 2000-2010
Summary Statistics
Variable Mean Median Standard
Deviation
Minimum Maximum
ln(m2r) 3.3947 3.364626 0.16055 3.153266 3.634094
ln(gdpr) 3.4672 3.46761 0.07518 3.36557 3.57123
tbr3 6.0864 6.36 2.18860 3.41 9.86
ln(p) 2.1115 2.11327 0.07566 2.00000 2.22037Number of observations = 11
12
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
13/24
V. RESULTS
OLS Results, Dependent Variable: Real Money Supply
Dependent Variable: LN_M2RMethod: Least Squares
Date: 03/11/11 Time: 22:16
Sample: 2000 2010
Included observations: 11
Variable Coefficient Std. Error t-Statistic Prob.
C -0.632805 0.769167 -0.822715 0.4378
LN_GDPR -0.093505 0.488637 -0.191358 0.8537
TBR3 -0.005529 0.003690 -1.498115 0.1778
LN_P 2.076881 0.469749 4.421255 0.0031
R-squared 0.994278 Mean dependent var 3.394737
Adjusted R-squared 0.991826 S.D. dependent var 0.160554
S.E. of regression 0.014516 Akaike info criterion -5.351859
Sum squared resid 0.001475 Schwarz criterion -5.207169
Log likelihood 33.43522 F-statistic 405.4533
Durbin-Watson stat 1.540820 Prob(F-statistic) 0.000000
Interpretation of coefficients:
ln(Mt) = -0.632805 - 0.093505ln(Yt) - 0.005529Rt + 2.076881ln(Pt)
The relationship between M and Y takes a double-log functional form. As these
results show, the elasticity of M with respect to Y is about -0.0935, suggesting that if
real national income goes up by 1 percent, on average, the real quantity of money
goes down by about 9.35 percent. Thus, the relationship between real quantity of
money and real national income is inversely proportional. The relationship between M and R takes a semilog, specifically a log-lin, functional
form. As these results show, an absolute change in the value of R results in a constant
proportional or relative change in M equal to the slope coefficient of R (i.e. 3).
The relationship between M and P takes a double-log functional form. As these
results show, the elasticity of M with respect to P is about 2.0769, suggesting that is
13
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
14/24
the price level goes up by 1 percent, on average, the real quantity of money goes up
by 207.69 percent. Thus the relationship between real quantity of money and price
level is directly proportional.
Test of Hypothesis for the Coefficients:
a. The tTest
1. Ho: j = 0
H1 : j = 0
2. Test statistics:
Variable t-Statistic Prob.
C -0.822715 0.4378
LN_GDPR -0.191358 0.8537
TBR3 -1.498115 0.1778
LN_P 4.421255 0.0031
3. Level of Significance: = 5%
4. Comparison oftstatistics with the critical tvalue:
Variable t-Statistic Critical tvalue
C -0.822715 2.306
LN_GDPR -0.191358 2.306TBR3 -1.498115 2.306
LN_P 4.421255 2.306
5. Decision:
At the 5% significance level, the critical t value corresponding to n = 11 and k= 3
is t0.025 (8) = 2.306. Since the explanatory variable LN_P is the only coefficient
whose t value is greater, in absolute value, than 2.365, it is the only significant
variable in explaining real money supply at the 5% level.
b. TheFTest
1. Ho : 2 = 3 = 4 = 0
H1 : There is a j = 0.
14
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
15/24
2. Test statistic:
F-statistic 405.4533
Prob(F-statistic) 0.000000
3. Level of Significance: = 5%
4. The criticalFvalue corresponding to the level of significance = 5%, n = 11, and
k= 3 is F0.05(2,8) = 4.46. Therefore, the computedFvalue (405.4533) is greater
than the tabulatedF0.05(2,8) = 4.46.
5. Decision:
Since the computed Fvalue is greater than the tabulated F value, we conclude
that the regression as a whole is significant at the 5% level.
Interpretation of R2 and R2:
a. Interpretation of the coefficient of determination,R2
Bet. LN_M2R and
LN_GDPR
Bet. LN_M2R and
TBR3
Bet. LN_M2R and
LN_P
R2 value 0.977310 0.688456 0.992405
The coefficient of determination between LN_M2R andLN_GDPR, R2 = 0.9773,
says that 97.73% of the variation in LN_M2R about its mean is explained by the
variation inLN_GDPR.
The coefficient of determination between LN_M2R and TBR3, R2 = 0.6885, says
that 68.85% of the variation in LN_M2R about its mean is explained by the
variation in TBR3.
The coefficient of determination between LN_M2R andLN_P, R2 = 0.9924, says
that 99.24% of the variation in LN_M2R about its mean is explained by the
variation inLN_P.
b. Interpretation of the adjusted coefficient of determination,R2
Adjusted R-squared 0.991826
15
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
16/24
The adjusted coefficient of determination, R2 = 0.9918, says that 99.18% of the
variation in LN_M2R about its mean is explained by the variation in its regressors
namelyLN_GDPR, TBR3, andLN_P.
Checking for Normality of Error Terms:
a. The Jarque-Bera Test
TheJB statistic of 8.565316 has ap-value of 0.013806. If the level of significance is =
1%, then, since 0.01
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
17/24
Detection of and Remedies for Problems in Linear Regression:
a. Multicollinearity
Detection through the Variance Inflation Factor (VIF):LN_GDPR
vs.
TBR3, LN_P
TBR3
vs.
LN_GDPR, LN_P
LN_P
vs.
LN_GDPR, TBR3
VIF 2.874 59.456 3.070
When using the Variance Inflation Factor (VIF) as an estimate of the increase in
the variance of an estimated coefficient due to multicollinearity, the higher the VIF
the more serious the multicollinearity problem is. Consequently, the regression results
indicate that the TBR3 variable is causing a serious multicollinearity problem.
Proposed Remedies:
i. In order to correct for the multicollinearity problem caused by the TBR3
variable, I will transform the functional form of my econometric model into the
following:
ln(Mt) = 1 + 2ln(Yt) + 3ln(Rt) + 4ln(Pt) + t
Regressing the new econometric model using OLS:
LN_GDPRvs.
TBR3, LN_P
TBR3vs.
LN_GDPR, LN_P
LN_Pvs.
LN_GDPR, TBR3
VIF 68.295 3.372 61.578
The OLS results show that the Variance Inflation Factor (VIF) of the TBR3
variable decrease from 59.456 to 3.372. However, after this change in functionalform, the VIFs of the GDPR andPvariables increase from 2.874 to 68.295 and from
3.070 to 61.578 respectively. Thus, the remedy of changing the functional form
presents another serious multicollinearity problem.
17
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
18/24
ii. In order to correct for the multicollinearity problem caused by the TBR3
variable, I will drop the TBR3 variable from the econometric model. Dropping the
TBR3 variable will result to the following econometric model:
ln(Mt) = 1 + 2ln(Yt) + 3ln(Pt) + t
Regressing the new econometric model using OLS:
LN_GDPR
vs.
TBR3, LN_P
LN_P
vs.
LN_GDPR, TBR3
VIF 59.456 59.456
The OLS results show that the Variance Inflation Factors (VIFs) of the GDPR and
P variables still remain high even after the TBR3 variable is dropped. Thus, the
remedy of dropping the TBR3 variable, still fails to correct the multicollinearity
problem.
In conclusion, since all the other remedies for multicollinearity (i.e. using priori
information, adding more observations, and using ridge regression and principal
components) have certain drawbacks, I choose to do nothing about the problem.
Inasmuch as there are no available additional data on all of the variables in the
econometric model, the remedy of adding more observation is clearly unfeasible.
Since the specification of the econometric model is theoretically correct, even with
multicollinearity, the estimators were BLUE. Nevertheless, dropping a variable that is
theoretically appropriate can lead to specification error, resulting in biased estimates
of the retained coefficients.
b. Serial Correlation
18
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
19/24
Detection through graphical method:
The graph ofetagainst et-1 suggests no clear evidence of a positive serial correlation.
Detection of Higher-Order Serial Correlation through the Breusch-Godfrey Serial
Correlation Test:
1. Ho : 1 = 2 = 3 = 4 = 0
H1 : There is at least one j not equal to zero.
2. Residuals et :
Observation Residual
1 0.00151
2 0.014503 0.00468
4 -0.00259
5 -0.00341
6 -0.03219
7 -0.00311
8 0.00828
9 0.00284
10 -0.00052
19
Plot ofe tagainst e t - 1
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
20/24
11 0.01001
3. Regression Analysis:
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.297145 Probability 0.864161
Obs*R-squared 3.121437 Probability 0.537713
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 03/22/11 Time: 07:24
Presample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
C 0.224687 1.438038 0.156245 0.8858
LN_GDPR -0.109794 0.956363 -0.114803 0.9159
TBR3 0.000473 0.005073 0.093210 0.9316
LN_P 0.071848 0.926279 0.077566 0.9431
RESID(-1) 0.077305 0.597207 0.129444 0.9052
RESID(-2) -0.390520 0.806201 -0.484395 0.6613
RESID(-3) -0.059606 0.588627 -0.101262 0.9257
RESID(-4) -0.570039 0.654511 -0.870939 0.4479
R-squared 0.283767 Mean dependent var -2.42E-16Adjusted R-squared -1.387443 S.D. dependent var 0.012145
S.E. of regression 0.018765 Akaike info criterion -4.958336
Sum squared resid 0.001056 Schwarz criterion -4.668957
Log likelihood 35.27085 F-statistic 0.169797
Durbin-Watson stat 2.291783 Prob(F-statistic) 0.974995
4. Test statistic: 2 = 3.121437
5. Level of Significance: = 5%
6. Decision:
Since the computed 2 = 3.121437 is less than the critical 2 (8) value = 15.5073 at
the significance level = 0.05, then the null hypothesis cannot be rejected at the
said significance level. Consequently, there is no evidence of a serial correlation
up to the fourth order (i.e.p = 4).
20
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
21/24
c. Heteroskedasticity
Detection through Whites Heteroskedasticity Test:
1. Ho : There is no heteroskedasticity.
H1 : There is heteroskedasticity.
2. Residuals:
Observation Residual
1 0.00151
2 0.01450
3 0.00468
4 -0.00259
5 -0.00341
6 -0.03219
7 -0.00311
8 0.008289 0.00284
10 -0.00052
11 0.01001
3. Regression output:
R2 = 0.235
m = 3
4. Test statistic:
White Heteroskedasticity Test:
F-statistic 0.205172 Probability 0.957069
Obs*R-squared 2.588662 Probability 0.858416
4. Level of Significance: = 5%
5. Decision:
Since the value of the test statistic Obs*R-squared = 2.588662 is less than the
critical 2
(8) value = 15.5073 at the significance level = 0.05, then the nullhypothesis cannot be rejected at the said significance level.
Alternatively, since thep-value of the test statistic Obs*R-squared = 0.858416 is
greater than the significance level = 0.05, then the null hypothesis cannot be
rejected at the said significance level.
Consequently, there is no evidence that the error terms t are heteroskedastic.
21
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
22/24
VI. SUMMARY AND CONCLUSION
The empirical analysis results show that the demand for money function is well specified.
The Jarque-Bera test verifies the normality of the error terms in the econometric model. The
regression, as a whole, is significant and explains much of the variation in the real quantity of
money. However, the ttest shows that the only significant variable in explaining the real quantity
of money is the price level. Put in other words, changes in the price level account for the
majority of changes in the real quantity of money. Nevertheless, changes in the real national
income and interest rates also contribute, although to a lesser extent, to the variation in real
quantity of money.
With regards to diagnosing and treating problems in linear regression, I test for problems
in multicollinearity, serial correlation and heteroskedasticity. Through the Variance Inflation
Factor (VIF), I arrive at the conclusion that the interest rates explanatory variable is the one
causing the multicollinearity problem. However, even though the problem of multicollinearity
exists, the estimators are still BLUE since the specification of the econometric model is correct.
Moreover, the Breusch-Godfrey Serial Correlation Test shows that there is no evidence of a
serial correlation up to the fourth order (i.e. p = 4). With regards to detecting the problem of
heteroskedasticity, I use the Whites Heteroskedasticity Test and arrive at the conclusion that the
error terms t are not heteroskedastic.
The conclusions above are subject to a number of limitations. First, it is unclear as to
what extent the results can be generalized to other countries. Each country has different data for
the explanatory variables used and thus the results generated may be far different for the cases of
other countries. Second, the error terms for each variable can be correlated over time. For
example, if demand for money increases one year given a level of national income, interest rates
and price level, demand for money will likely increase in the following year as well. Therefore,
the estimation procedure may need to correct for this autocorrelation. Third, the number of
observations available is limited making trend analysis rather difficult. Finally, there may be
22
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
23/24
other variables that affect the demand for money (e.g. poverty rate or government expenditure).
Including these in the regression may increase the precision of my estimates as well as eliminate
potential omitted variable bias. Nevertheless, considerations of these shortcomings are left for
future research.
23
8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final
24/24
REFERENCES
Book
Rolando A. Danao:Introduction to Statistics and Econometrics, University of the
Philippines Press, 2002
Damodar N. Gujarati: Basic Econometrics, McGraw-Hill/Irwin, 2003, fourth
edition
J. Maravic, M. Palic:Econometric Analysis of Money Demand in Serbia, National
Bank of Serbia Research Department Discussion Paper, April 2005
I. Takeshi, H. Shigeyuki:An empirical analysis of the money demand function in
India, Institute of Developing Economies (IDE) Discussion Paper No. 166. 2008.9,
September 2008
Website
htttp://www.adb.org/Statistics
http://www.bsp.gov.ph/statistics/sdds/dcs.htm
http://www.bsp.gov.ph/statistics/spei_new/tab46.htm
http://www.indexmundi.com/philippines/gdp_per_capita_(ppp).html
http://moneysense.com.ph/investing/government-securities-gs-investing-101/
http://www.bsp.gov.ph/statistics/sdds/tbillsdds.htm
http://www.nscb.gov.ph/stats/tbills.asp
http://tutor2u.net/economics/content/topics/monetarypolicy/demand_for_money.h
tm
http://internationalecon.com/Finance/Fch40/F40-6.php
http://internationalecon.com/Finance/Fch40/F40-7.php
24
http://www.bsp.gov.ph/statistics/sdds/dcs.htmhttp://www.bsp.gov.ph/statistics/spei_new/tab46.htmhttp://www.indexmundi.com/philippines/gdp_per_capita_(ppp).htmlhttp://moneysense.com.ph/investing/government-securities-gs-investing-101/http://www.bsp.gov.ph/statistics/sdds/tbillsdds.htmhttp://www.nscb.gov.ph/stats/tbills.asphttp://tutor2u.net/economics/content/topics/monetarypolicy/demand_for_money.htmhttp://tutor2u.net/economics/content/topics/monetarypolicy/demand_for_money.htmhttp://internationalecon.com/Finance/Fch40/F40-6.phphttp://internationalecon.com/Finance/Fch40/F40-7.phphttp://www.bsp.gov.ph/statistics/sdds/dcs.htmhttp://www.bsp.gov.ph/statistics/spei_new/tab46.htmhttp://www.indexmundi.com/philippines/gdp_per_capita_(ppp).htmlhttp://moneysense.com.ph/investing/government-securities-gs-investing-101/http://www.bsp.gov.ph/statistics/sdds/tbillsdds.htmhttp://www.nscb.gov.ph/stats/tbills.asphttp://tutor2u.net/economics/content/topics/monetarypolicy/demand_for_money.htmhttp://tutor2u.net/economics/content/topics/monetarypolicy/demand_for_money.htmhttp://internationalecon.com/Finance/Fch40/F40-6.phphttp://internationalecon.com/Finance/Fch40/F40-7.php