the relationship between nominal gdp on money growth

8/2/2019 The Relationship Between Nominal GDP on Money Growth

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ECONOMETRICS PRESENTATION

GROUP MEMBER

MAYA EVARINA BINTI PAILINCHRISSY PAULUS

CHARLOVIE CHARLES


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The RELATIONSHIP Between

ECONOMIC GROWTH on MONEY

and INFLATION


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Introduction

The view that low inflation is an importantrequirement for sustained economic growth becamewidely accepted after the great depression in the1930s.

Thus, low inflation is always considered as anobjective of economic policy, it has been

shown that volatility reduces economic growth and

is therefore worthy of our attention (Klomp andHaan, 2009).

Furthermore, it is likely that inflation and moneydisproportionately affects the economic growth.


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Literature Review

Erbaykal and Okuyan (2008) examined therelationship between the inflation and the economicgrowth . (2001), and the existence of a cointegration

relationship between the two series was detectedfollowing the test result.Whereas no statistically significant long termrelationship was foundwith the formed ARDLmodels, a negative and statistically significant shortterm relationship has been found.Whereas nocausality relationship was found from economicgrowth to inflation, a causality relationship wasfound from inflation to economic growth.


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Asogu (1998) examined the influence of moneysupply and government expenditure on GrossDomestic Product. The model assumed theirrelevance of anticipated monetary policy for

short run deviations of domestic output from itsnatural level. The result indicated thatunanticipated growth in money supply wouldhave positive effect on output. A clear

examination of the above shows that there is nogeneral agreement on the determinant ofeconomic growth.


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Theoretical Framework

A macroeconomic policy that seeks to expandthe money supply to encourage economic growthor reduce inflation (price increases). One form ofexpansionary policy is fiscal policy, which comes

in the form of tax cuts, rebates and increasedgovernment spending. Expansionary policies canalso come from central banks, which focus on

increasing the money supply in the economy.


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Data and Methodology

The Data that we use for this analysis is timeseries data from 1972-2005.Multiple Regression ModelPark TestTesting Hypothesis for Individual Partial

Regression CoefficientWhite Test

Joint Hypothesis That B2=B3=0 or R

2

=0Jarque-Bera (JB) TestRamsey RESET TestBreusch-Godfrey Serial Correlation LM Test

Durbin-Watson Test


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MULTIPLE REGRESSION MODELDependent Variable: GROWTH

Method: Least Squares

Date: 12/16/11 Time: 00:07

Sample: 1972 2005

Included observations: 34

Variable Coefficient Std. Error t-Statistic Prob.

C 6.728047 1.870676 3.596586 0.0011

INFLATION -0.289302 0.176432 -1.639738 0.1112

MONEY 0.133137 0.074553 1.785811 0.0839

Y = 6.728047 - 0.176432X2 + 0.133137X3

Se = (1.870676) (0.176432) (0.074553)

T = (3.596586) (-1.639738) (1.785811)

P = (0.0011) (0.1112) (0.0839)

R2 = 0.154716

Growth = 6.728047- 0.289302Inflation + 0.133137Money

These results shows that both the slope coefficient are positive. The positive coefficient of

Inflation suggest that 1% increase in inflation leads, on average, would reduce growth by 0.289

(when money is constant). On the other hand, an increase in money supply, on average, leads

to increase the rate of growth by 0.133.


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Testing Hypothesis for Individual

Partial Regression

Coefficient(Inflation)

Ho: B3 = 0

H1: B3 > 0

Decision rule: Ho is rejected if the calculated p-value is less than alpha at 5% significant level, Ho is

rejected.

Result : Since the calculated p-value is 0.1371 > 0.05 at significant level, Ho is not rejected.

Conclusion : The result shows there is no relationship between growth and money

Dependent Variable: GROWTH


Date: 12/16/11 Time: 09:29

Sample: 1972 2005



C 5.229015 1.728007 3.026038 0.0049

INFLATION 0.024543 0.016093 1.525065 0.1371


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Testing Hypothesis for Individual

Partial Regression Coefficient(Money

Ho: B2 = 0 (There is no relationship between Growth and Money)

H1: B2 > 0 (There is a positive relationship between Growth and Money)

Decision rule: Ho is rejected if the calculated p-value is less than alpha at 5% significant level, Ho is

rejected.

Result : Since the calculated p-value is 0.1019 > 0.05 at significant level, Ho is not rejected.

Conclusion : The result shows there is no relationship between Growth and Money.

Dependent Variable: GROWTH


Date: 12/15/11 Time: 22:43

Sample: 1972 2005



C 5.136888 1.640971 3.130395 0.0037

MONEY 0.011367 0.006750 1.683943 0.1019


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Testing The Joint Hypothesis That B2=B3=0 or R2=0

=R2 /(K 1)

1 R2 /(n k)

Where n=the number of observation and k=the number of explanatory variable including the

intercept.

Ho: R2=0 (There is no difference in y on X2 and X3)

Ho: R2 0 (There is a difference in y on X2 and X3)

Decision rule: If the calculated F value is greater than critical value, Ho is rejected.

Result:

=0.154716/(3 1)

1 0.154716/(34 3)

=0.077358

0.995009161

= 0.078

Degree of freedom in the numerator, k-1 = 3-1=2

Degree of freedom in the denominator, n-k = 34-3=31

The larger the R2, the larger the F value. But, E-view found that F value is smaller according to

weak R2. Since, the computed F value is 0.078 which is less than the critical value of 3.23,

d.f.(2,31) thus the null hypothesis is not rejected.

Conclusion: The result shows that there is no difference in economic growth on Money and

Inflation.


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Test multicollinearity of pairwise between money and

inflation, and between inflation and moneya)Regress lnmoney (lnx3) on the remaining

lninflation(lnx2) and obtain the coefficient ofdetermination , sayslnX3 = 0.401680 + 1.087049lnX2

t = (4.535156) (55.38782)

se = (0.088570) (0.019626)R2 = 0.989677

b)Regress lninflation (lnX2) on the remaininglnmoney (lnX3) and obtain the coefficient of

determination , says .lnX2 = -0.319295 + l0.910425nX3

t = (-3.660873) (55.38782)se = (0.0009) (0.0000)

R2

= 0.989677


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Ho: VIF < 10, There is no serious multicollinearityproblemH1: VIF > 10, There is serious multicollinearity problem

Decision rule, if VIF is greater than 10, there is seriousmulticollinearity problem.

Result : VIF = 96.87

Since, the computed VIF is 96.87 which is greater than10, there is serious multicollinearity problem.

Conclusion: The result shows that this regression suffermulticollinearity and coefficient of money and inflationturned out to be statistically significant.


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Park TestDependent Variable: SS1


Date: 12/16/11 Time: 00:14

Sample: 1972 2005



C 56.43030 28.05353 2.011522 0.0528

GROWTHF -5.444487 3.594874 -1.514514 0.1397

Ho: No Heteroscedasticity problem

H1: There is a heteroscedasticity problem

Decision rule: Ho is rejected if the calculated p-value is less than alpha at 5% significant level,

Ho is rejected.

Result: Since the calculated p-value is 0.1397>0.05 at 5% significant level, Ho is not rejected.

Conclusion: This model is having heteroscedasticity problem.


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Jarque-Bera (JB) Test

Ho: The error term is normally distributed

H1: The error term is not normally distributed

Decision rule: Ho is rejected if the calculated p-value is less than alpha at 5% significant level,Ho is rejected.

Result: Since the calculated p-value is 0.000012


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White Test

Heteroskedasticity Test: White

F-statistic 1.176781 Prob. F(5,28) 0.3453

Obs*R-squared 5.904066 Prob. Chi-Square(5) 0.3157

Scaled explained SS 13.35537 Prob. Chi-Square(5) 0.0203

Ho: The variance of distribution or error term is constant (homocedasticity)

H1: The variance of distribution or error term is not constant (heteroscedasticity


Ho is rejected.

Result: Since, we obtain n.R25.90 with 2 d.f. This chi-square value has a probability of about

0.3157> 0.05 at significant level, Ho is not rejected.

Conclusion: This model shows that the variance of error term is constant.


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RAMSEY RESET TestRamsey RESET Test:


Log likelihood ratio 0.391608 Prob. Chi-Square(1) 0.5315

Ho: No misspecification error

H1: There is misspecification error


Ho is rejected.

Result: Since, the calculated probability chi-square is 0.5315 which is greater than 5% atsignificant level, Ho is not rejected.

Conclusion: This model shows no misspecification error.


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Breusch-Godfrey Correlation LM TestsBreusch-Godfrey Serial Correlation LM Test:


Obs*R-squared 2.337643 Prob. Chi-Square(2) 0.3107

Ho: No autocorrelation in the distribution or error term

H1:There is autocorrelation in the distribution error term


Ho is rejected.

Result: Since, the calculated probability chi-square is 0.3107 which is greater than 5% at

significant level, Ho is not rejected.

Conclusion: The result shows there is no autocorrelation in the error term


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Durbin-Watson d TestsDependent Variable: GROWTHMethod: Least Squares

Date: 12/16/11 Time: 00:07

Sample: 1972 2005


R-squared 0.154716 Mean dependent var 7.626956

Adjusted R-squared 0.100181 S.D. dependent var 4.262394

S.E. of regression 4.043255 Akaike info criterion 5.716074

Sum squared resid 506.7851 Schwarz criterion 5.850753

Log likelihood -94.17326 Hannan-Quinn criter. 5.762004

F-statistic 2.837026 Durbin-Watson stat 2.344978

Prob(F-statistic) 0.073883

0 1.321 1.577 2 2.423 2.679

~ = 2.344978

N = 33

= 2

=1.577

= 1.321

Decision rule: d test

Null hypothesis Decision If

No positive autocorrelation Reject 0


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Result & Discussion

By using the normality test, The regress errorterm is not normally distributed and there is alsono error of measurement.

The white test shows that the variance of

distribution or error term is constant(homocedasticity)

Breusch-Godfrey shows there is noautocorrelation in the error term.

Durbin Watson shows no positive and negativeautocorrelation.

the relationship between nominal gdp on money growth

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