the relationship between nominal gdp on money growth
TRANSCRIPT
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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
Method: Least Squares
Date: 12/16/11 Time: 09:29
Sample: 1972 2005
Included observations: 34
Variable Coefficient Std. Error t-Statistic Prob.
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
Method: Least Squares
Date: 12/15/11 Time: 22:43
Sample: 1972 2005
Included observations: 34
Variable Coefficient Std. Error t-Statistic Prob.
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
Method: Least Squares
Date: 12/16/11 Time: 00:14
Sample: 1972 2005
Included observations: 34
Variable Coefficient Std. Error t-Statistic Prob.
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
Decision rule: Ho is rejected if the calculated p-value is less than alpha at 5% significant level,
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:
F-statistic 0.347534 Prob. F(1,30) 0.5599
Log likelihood ratio 0.391608 Prob. Chi-Square(1) 0.5315
Ho: No misspecification error
H1: There is misspecification error
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 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:
F-statistic 1.070540 Prob. F(2,29) 0.3560
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
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 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
Included observations: 34
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.