An Empirical Analysis of the Money Demand Function in the Philippines_Final

8/4/2019 An Empirical Analysis of the Money Demand Function in the Philippines_Final

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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

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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

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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.

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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

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Scatter Plot of T-Bond Yieldand M1 per dollar of GDP

Scatter Plot of T-Bill Yieldand M1 per Dollar of GDP


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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.

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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.

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Interest

Rate (r)

9%

7%

5%

Real Money Demand

Money

Demand


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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

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Interest

Rate (r)

9%

7%

5%

Real Money Demand

MD1

MD2


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The general approach I will be using in analyzing my data is as follows:

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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.


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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.

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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.

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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.

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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

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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

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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.

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2. Test statistic:

F-statistic 405.4533

Prob(F-statistic) 0.000000


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

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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


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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.

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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

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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

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Plot ofe tagainst e t - 1


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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


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).

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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


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.

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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

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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.

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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

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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

Documents

An Empirical Analysis of the Money Demand Function in the Philippines_Final