AUSTRALIA AND THE GFC (Empirical Analysis) 19-5-2015

19 May 2015

Australia and the Gfc

An empirical study on Australia’s battle against a recession

[ECONOMIC MODELLING] KRISTIAN STJELJA 17475903 LEEROY KHATTAR 17882789

19 May 2015

Contents1. Introduction....................................................................................................................................................2

Objective of the Study:................................................................................................................................................2

2. Literature Review............................................................................................................................................2

3. Specification of Model 1..................................................................................................................................3

4. Data Description.............................................................................................................................................4

5. OLS Analysis for Model 1.................................................................................................................................7

JB test, normality of errors test...................................................................................................................................7

White’s test, constant variance of errors test.............................................................................................................7

VIF test, Multicollinearity test.....................................................................................................................................8

DW test, errors are or are not related.........................................................................................................................8

Ramsay test, no omitted variables test.......................................................................................................................9

Model 1 conclusion.....................................................................................................................................................9

6. Specification of Model 2................................................................................................................................10

7. OLS Assumptions Model 2.............................................................................................................................14

JB test, normality of errors test.................................................................................................................................14

White’s test, constant variance of errors test...........................................................................................................15

VIF test, Multicollinearity test...................................................................................................................................15

DW test, errors are or are not related.......................................................................................................................15

Ramsay test, no omitted variables test.....................................................................................................................16

Model 2 Conclusion...................................................................................................................................................16

8. Conclusion.....................................................................................................................................................17

9. References....................................................................................................................................................19

Appendix B................................................................................................................................................................21

Appendix C................................................................................................................................................................23

KRISTIAN STJELJA 17475903 LEEROY KHATTAR 17882789 1

19 May 2015

1. Introduction

Objective of the Study:

The Global Financial Crisis, which struck the world in 2008, is considered the worst financial crisis since the great depression of the 1930’s. Although many economies around the world were greatly affected by the global financial crisis, Australia however emerged relatively unscathed. There are said to be several reasons attributed to Australia’s great escape, however for the purpose of this investigation, this paper aims to analyse the impact that monetary policy has had on Australia’s economic growth over the past decade. In the second quarter of 2007, prior to the Global Financial Crisis, Australia’s annual GDP growth had hit a peak of 5.4%. With looming inflationary concerns the Reserve Bank increased rates, so that by early 2008 the cash rate hit a high of 7.25%. In August 2007, when BNP Paribas announced that it was ceasing activity in three hedge funds that specialised in US mortgage debt, it became painfully clear that there were tens of millions of dollars’ worth of dodgy derivatives circulating around that were worth much less than bankers had previously imagined. In September 2008, the US government allowed the investment bank Lehman Brothers to go bankrupt and as result the threat of a domino effect throughout the global financial system was apparent. Governments were forced to inject vast sums of capital into their banks to prevent them from collapsing as well. However this unfortunately did not prevent the global economy from plummeting. The global financial crisis peaked in October 2008 and as result Australia experienced negative growth in the 4th quarter of 2008. The Reserve Bank’s response to the volatile world economy was swift, with the immediate implementation an expansionary stance on monetary policy in the wake of the Lehman Brothers collapse in September. Over the months in the final quarter of 2008, the RBA continued to hack down the cash rate by a combined total of 2.75%, in order to spur activity within the economy. By the first quarter of 2009, Australia’s gross domestic product grew by 1%. The RBA continued to slash at the cash rate by a further 1.25% so that the cash rate was at 3% by the end of the second quarter of 2009. This translated to positive economic growth over the corresponding periods and thereafter.

2. Literature Review“The Impact of Macroeconomic Variables on Gross Domestic Product: Empirical Evidence from Ghana” (Agalega, E & Antwi, S 2013), found the variables interest rates and inflation to be strongly related to the GDP growth in Ghana. The model had an R2 value of 0.435. Simply put, over the period of 1980 -2010, inflation and interest rates accounted for or explained 44% of the changes in the GDP of Ghana (Agalega, E & Antwi, S 2013). The study also achieved the correct signs. Therefore as inflation increased, so did GDP and as interest increased, GDP decreased. Our model will look to test similar variables, however instead of using inflation as a separate independent variable, we will instead transform all data into real figures.


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3. Specification of Model 1Our original model is as follows:

ln ∆Y=β0+ β1 ln∆ X 1+β2 ln∆ X2+β 3 ln∆ X 3+ε

ln ∆GrowthRt=β 0+β1 ln∆ Rate R t+β2 ln∆ConsumRt+β3 ln∆InvestRt

∆GrowthRt= Change in Real Gross Domestic Product Growth in period t∆ RateRt = Change in the Real Cash Rate in period t∆ConsumRt= Change in real consumption in period t∆ InvestRt = Change in real final private investment in period tε = Random Error Term

We have chosen these variables and have predicted the expected signs according to research, past literature and economic theory.

In the situation that the economy were to experience a contractionary output gap, the Reserve Bank of Australia, through the use of open-market operations, can target a lower overnight cash interest rate. Movements in the cash rate are quickly passed through to other capital market interest rates, including deposit and lending rates. Lower interest rates would mean that households will have less to pay on loans, increasing their level of disposable income. Firms are also more inclined to borrow funds for the purpose of investment. The combined affect will increase planned aggregate expenditure and the economy will return to equilibrium at its potential level of output. However in the situation the economy was to expand too rapidly, resulting in inflationary pressures, the RBA, can target a higher overnight cash-rate in order to reduce aggregate demand and thus reduce the level of economic growth. Therefore, because an increase in the cash rate results in a decrease in the GDP growth rate, it is expected that the sign for the variable ‘RateR’ will be negative.

Since the variables ‘ConsumR’ and ‘InvestR’ are both components of the expenditure equation used to measure GDP (where GDP = C + I + G + NX), it is expected that the relevant signs for both of these variables will be positive. An increase in consumption and/or invest over period t will increase the amount of goods produced in an economy during period t.


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4. Data Description

For the purpose of this investigation, the collected time-series data is measured on a quarterly basis across the past 10 years (2004-2014). Although adjustments made to the cash rate are made on a monthly basis, data collected for other variables could only be found on a quarterly basis. Therefore to achieve a consistent time lapse across the data set, monthly adjustments made to the cash rate within each corresponding quarter of the year were accumulated. Thus data collected for the cash rate represents the aggregate adjustments made over that quarter period. In order to avoid stationary problems, all data sets represent the percentage changes of each variable from the previous period. Therefore the functional form of our model is log-log. Additionally all data sets have been transformed into real terms. The collected data has therefore been adjusted for inflation in order to provide a more accurate analysis of the relationship between the dependent and independent variables. Nominal data for the variables GrowthR, ConsumR and InvestR were obtained via the Australia Bureau of Statistics (ABS). The inflation rate was also obtained via the ABS and captures the quarterly percentage changes of the Consumer Price Index (where the base year is 2012). The nominal figures obtained for GrowthR, ConsumR and InvestR were then multiplied by [1 – rate of inflation in corresponding period]. The changes in the nominal cash rate were obtained via the Reserve Bank of Australia. This data was then also converted into real terms in order to better reflect the economic cost of borrowing.

TABLE 1


Model 1

Constant 0.0013544 (0.011572)

RateR -0.388927 (0.1623991)

ConsumR 0.706319 (0.1115546)***

InvestR 0.0013544 (0.011572)

Diagnostic tests

R2 0.5437

Adjusted R2 0.5094

FStat 15.94 ***

White’s Test 6.70

JB Test -0.5035964429

DW

Ramsay Rest Test 0.56

VIF (RateR) (ConsumR) (InvestR)

1.11

1.091.121.13

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log (Growth) = 0.0013544 - 0.388927RateR + 0.706319ConsumR + 0.0013544InvestR

Se = (0.0010923) (.1623991) (.1115546) (.0217696)

P = 0.249 0.812 0.000 0.391

F = 15.88

R2 = 0.5437

N = 44

The model has an R2 of 0.5437, inferring that the independent variables ‘Rate’, ‘ConsumR’ and ‘InvestR’ explain 54.37% of the dependent variable ‘GrowthR’.

To determine the individual significance of the variables, we have used the t-test with significance levels of 1%, 5% and 10%.

At 1% significance: Reject the null hypothesis if t-score < -2.704 OR t-score > +2.704At 5% significance: Reject the null hypothesis if t-score < -2.021 OR t-score > +2.021At 10% significance: Reject the null hypothesis if t-score < -1.684 OR t-score > +1.684Variable Null Hypothesis Alternative

Hypothesis t-score Critical

t-valueConclusion

RateR H0 : β1 = 0 H1 : β1 ≠ 0 -0.37 1% = 2.7045% = 2.02110% = 1.684

Accept H0Accept H0Accept H0Thus not sig

ConsumR H0 : β2 = 0 H0 : β2 ≠ 0 6.31 1% = 2.7045% = 2.02110% = 1.684

Recject H0Reject H0Reject H0Sig at all levels

InvestR H0 : β3 = 0 H0 : β3 ≠ 0 0.90 1% = 2.7045% = 2.02110% = 1.684


Therefore based on the t-stat test we can conclude that the only significant variable is ConsumR. This conclusion is reinforced by the P-value of ConsumR, where P = 0.000 < α = 0.05 indicating that the variable is significant. RateR has a P-value of 0.712 and thus is not significant, since 0.0712 > α = 0.05. Likewise, InvestR is not a significant variable as it has a P-value of 0.281, and 0.0281 > α = 0.05.


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To test the overall significance of the model we use the F-test.

Reject the null hypothesis if the calculated F-score > Critical F-scoreVariable Null Hypothesis Alternative

Hypothesis F-score Critical

F-valueConclusion

1% H0 : β1 = β2 = β3 = 0 H1 : At least one ≠ 0 15.94 4.31 Reject H0Model Sig



Since our Fstat 15.94 is greater than our Fcv at all significance levels, we can conclude that overall model is significant. This is determined by the upper percentage points of F distribution table, where N1 = 3 N2 = 37.


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5.OLS Analysis for Model 1

Appendix B: Decision table

Appendix C: Stata output for Model 1

JB test, normality of errors testThe Jarque–Bera test hinges on the null hypothesis that the sample skewness and the sample excess Kurtosis are jointly zero and thus match a Normal distribution (Eder, Keiler and Pichl, n.d.).

H0 : E (e) = 0 Errors are normally distributed HA : E (e) ≠ 0 Errors not normally distributed

JBstat = N6

∗¿

Where S = -.1575129 and K = 2.403877

JBstat = -0.5035964429

Since JBstat = -0.5035964429 < 5.99 we can accept H0 and conclude that the errors are normally distributed and unbiased.

White’s test, constant variance of errors test

The White’s test tests for heteroskedasticity. Heteroskedasticity occurs when the distribution or the variance of the residuals changes along with the independent variables. Homoskedasticity on the other hand, is the assumption that the error term has a constant variance (Woolridge et.al 2012). Homoskedasticity must occur in order to satisfy OLS assumptions.

H0 : Var (e) = σ2 Homoskedastic HA : Var (e) ≠ σ2 Heteroskedastic

If P < α Reject H0

P-value = 0.6679 Significance level α = 0.05

Since Pstat 0.6679 > α = 0.05 we cannot reject H0 and thus can conclude that the errors have a constant variance.


http://link.springer.com/search?dc.title=Kurtosis&facet-content-type=ReferenceWorkEntry&sortOrder=relevance

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VIF test, Multicollinearity test

We use the VIF test to test for Multicollinearity. Multicollinearity occurs when there is a fairly strong linear relationship among a set of explanatory variables (Albright, Winston and Zappe, 2010).

H0 : Cov (x1,x2) = 0HA : Cov (x1,x2) ≠ 0

If VIF > 10 Reject H0

Since VIF for all variable is < 10 (refer to table 1) we can accept H0 and therefore conclude that multicollinearity is inexistent within the model, inferring that the chosen variables are not closely related.

The model can therefore be considered unbiased as it satisfies the 3 previous assumptions

DW test, errors are or are not related

2.199332

Durbin-Watson statistics are widely used as tests for autocorrelation (Cohen, 1996). Autocorrelation most often occurs in time series data where the observation at a given point in time is dependent on the observations from the previous time periods (Ajmani, 2008).

DL = 1.336 DU = 1.720K=3 N= 44Significance level α = 0.05

H0 : Cor (ei, ej) = 0 No autocorrelation HA : Cor (ei, ej) ≠ 0 ± autocorrelation

The calculated Durbin-Watson statistic 2.199332 falls in the no autocorrelation area. We thus accept H0 and conclude that there is no autocorrelation amongst the data chosen.


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Ramsay test, no omitted variables test

The Ramsey Reset Test tests whether non-linear combinations of the fitted values help explain the dependent variable. The intuition behind the test is that if non-linear combinations of the independent variables have any power in explaining the dependent variable, the model is mis-specified.

In order to determine whether our specified model had misspecification, we tested for the functional form of GrowthR.

H0 : No mis-specification HA : Mis-specification

If P-value > α = 0.05Reject H0

Since PStat = 0.6448 > 0.05 we accept H0 and conclude that the model has no mis-specification. Thus our model does not have any omitted variables.

Model 1 conclusion Since our model satisfies all relevant test for the OLS assumptions we can conclude that the model is unbiased and efficient. Our model this therefore the Best Linear Unbiased Estimator (BLUE).

However since only 1 variable (ConsumR) is significant and our R2 is 0.5437, we will run a second model to analyse what other variables could explain the change in real economic growth.


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6. Specification of Model 2Our second model is as follows:

ln ∆Y=β0+ β1 ln∆ X 1+β2 ln∆ X2+β 3 ln∆ X 3+β 4 ln∆ X 4+ β5 ln∆ X 5+β 6 ln∆ X 6+ε

ln ∆GrowthR=β 0−β 1 ln∆ Rate+β 2 ln∆ConsumR+β 3 ln∆ InvestR+¿ β3 ln∆GovR + β3 ln∆ NXR−β3 ln∆ EXGR

∆GrowthR= Change in Real Gross Domestic Product Growth∆ RateR = Change in the Real Cash Rate ∆ConsumR= Change in real consumption ∆ InvestR = Change in real final private investment ∆GovR = Change in real government expenditure ∆NXR = Change in Real Net Exports∆EXGR = Change in exchange rate (AUD:USD) ε = Random Error Term

In October 2008, the Australian government announced a $10.4 billion stimulus package in order to spur spending within the economy. This was then followed by an additional $42billion stimulus package, announced in February 2009. It is still greatly debated whether these stimulus packages were a defining factor that helped save Australia from a recession following the global financial crisis. However since Government spending is a component of the expenditure equation used to measure GDP (where GDP = C + I + G + NX), an increase in government spending should increase the level of GDP within an economy. Hence the variable ‘GovR’ representing the change in final government expenditure, is used in the model to test the significance that government spending has had over the period of 2004-2014. Since an increase in government spending should translate to an increase in GDP, the predicted sign is positive.

Another attributed factor that helped save Australia from a recession in the aftermath of the Global financial Crisis was the resources boom that followed. Backed by strong demand from China and India, the resources boom provided Australia with its longest sustained terms of trade in its history. An increasing terms of trade reflects greater demand for the country's exports. In turn, this results in rising revenues from exports, which provides increased demand for the country's currency. Therefore in order to measure the demand for our commodities, the variables ‘NXR’ (Change in Real Net Exports) and ‘EXGR’ (Change in Exchange rates) were used. After producing negative net exports for the first 3 quarters of 2008, Australia recorded positive figures for the following 8 periods. As a result of increased demand for our exports, Australia’s dollar appreciated from 0.6714USD in the 4th quarter of 2008 to a high of 1.0615USD in the second quarter of 2011.Since Australia has a floating exchange rate, the exchange rate is an automatic stabiliser. Automatic stabilisers help balance out fluctuations in the business cycle. Theoretically if the Australian dollar appreciates, it will result in imports being cheaper and export being more expensive. Thus consumers are


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likely to shop for products abroad and businesses are more likely to source their inputs for production from overseas. Therefore Net exports (NXGR) will decrease, reducing the level of aggregate demand within the economy and thus decrease the level of GDP growth. Exchange rates should therefore move in the opposite direction to economic growth and hence the sign is expected to be negative. Net Exports (NXR), being a component of the expenditure equation, is expected to have a positive relationship with GDP.

Table 2


Model 2

Constant 0.003174 (0.0013577)

Cash rate (RateR) -0.0082266(0.0115058)

Real Consumption(ConsumR) 0.6528628(0.1211729)

Real Investment (InvestR) 0.0194137(0.021871)

Real Government (GovR) 0.1853233(0.1027819)

Real Net Export (NXR) 0.0000674 (0.00001828)

Real Exchange Rate (EXGR) 0.0118996 (0.0184613)

Diagnostic tests

R2 0.5815

Adjusted R2 0.5137

FStat 8.57

White’s Test 29.81

JB Test -0.10037573

DW 2.180971

Ramsay Rest Test 29.81

VIF (EXGR) (RateR) (GovR) (ConsumR) (NXR) (InvestR)

1.921.821.401.301.181.13

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log (Growth) = 0. 003174 - 0. 0082266RateR + 0. 6528628ConsumR + 0. 0194137InestR + 0.1853233GovR + 0.0000674NXR + 0.0118996EXGR

Se = (0.0013577) (0.0115058) (0.1211729) (0.021871) (0.1027819) (0.00001828) (0.0184613)

P = 0.479 0 0.380 0.080 0.714 0.523 0.816

F = 8.57

R2 = 0.5815

N = 44

The model has an R2 of 0.5815, inferring that the independent variables ‘RateR’, ‘ConsumR’, ‘InvestR’, ‘GovR’, ‘NXR’ and ‘EXGR’ explain 58.15% of the dependent variable ‘GrowthR’. Although model 2 has an additional 3 variables to that of model 1, these additional variables only explain a further 3.78% of the dependent variable ‘GrowthR’.

To determine the individual significance of the variables, we have used the t-test with significance levels of 1%, 5% and 10%.

At 1% significance: Reject the null hypothesis if t-score < -2.704 OR t-score > +2.704At 5% significance: Reject the null hypothesis if t-score < -2.021 OR t-score > +2.021At 10% significance: Reject the null hypothesis if t-score < -1.684 OR t-score > +1.684Variable Null Hypothesis Alternative

Hypothesis t-score Critical

t-valueConclusion

RateR H0 : β1 = 0 H1 : β1 ≠ 0 -0.71 1% = 2.7045% = 2.02110% = 1.684


ConsumR H0 : β2 = 0 H0 : β2 ≠ 0 5.39 1% = 2.7045% = 2.02110% = 1.684

Recject H0Reject H0Reject H0Sig at all levels

InvestR H0 : β3 = 0 H0 : β3 ≠ 0 0.89 1% = 2.7045% = 2.02110% = 1.684


GovR H0 : β4 = 0 H0 : β4 ≠ 0 1.80 1% = 2.7045% = 2.02110% = 1.684


NXR H0 : β5 = 0 H0 : β5 ≠ 0 0.37 1% = 2.7045% = 2.021

Accept H0Accept H0


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10% = 1.684 Accept H0Thus not sig

EXGR H0 : β6 = 0 H0 : β6 ≠ 0 0.64 1% = 2.7045% = 2.02110% = 1.684


Therefore based on the t-stat test we can conclude that once again the only significant variable is ConsumR. This conclusion is reinforced by the P-value of ConsumR, where P = 0.000 < α = 0.05 indicating that the variable is significant. RateR has a P-value of 0.712 and thus is not significant, since 0.0712 > α = 0.05. Likewise, InvestR is not a significant variable as it has a P-value of 0.281, and 0.0281 > α = 0.05.

To test the overall significance of the model we use the F-test.

Reject the null hypothesis if the calculated F-score > Critical F-scoreVariable Null Hypothesis Alternative

Hypothesis F-score Critical

F-valueConclusion




Since our Fstat 8.57 is greater than our Fcv at all significance levels, we can conclude that overall model is significant. This is determined by the upper percentage points of F distribution table, where N1 = 6 and N2 = 37.


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7.OLS Assumptions Model 2

Appendix A: Decision table

Appendix C: Stata output for Model 2

JB test, normality of errors test

H0 : E (e) = 0 Errors are normally distributed HA : E (e) ≠ 0 Errors not normally distributed

JBstat = N6

∗¿

Where S = -0.2939077 and K = 2.460763

JBstat = -0. 10037573

Since JBstat = -0. 10037573 < 5.99 we can accept H0 and conclude that the errors are normally distributed and unbiased.

010

2030

4050

Den

sity

-.02 -.01 0 .01 .02 .03GrowthR

Kernel density estimateNormal density

kernel = epanechnikov, bandwidth = 0.0032

Kernel density estimate


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White’s test, constant variance of errors test

H0 : Var (e) = σ2 Homoskedastic HA : Var (e) ≠ σ2 Heteroskedastic

If P < α Reject H0

P-value = 0.3229 Significance level α = 0.05

Since Pstat 0.3229 > α = 0.05 we cannot reject H0 and thus can conclude that the errors have a constant variance.

VIF test, Multicollinearity test

H0 : Cov (x1,x2) = 0HA : Cov (x1,x2) ≠ 0

If VIF > 10 Reject H0

Since VIF for all variables is < 10 (refer to table 2) we can accept H0 and therefore conclude that multicollinearity is inexistent within the model, inferring that the chosen variables are not closely related.

The model can therefore be considered unbiased as it satisfies the 3 previous assumptions

DW test, errors are or are not related

K=7 N=44

Significance level α= 0.05%

DL=1.189 DU=1.895


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H0 : Cor (ei, ej) = 0 No autocorrelation HA : Cor (ei, ej) ≠ 0 ± autocorrelation

The calculated Durbin-Watson statistic 2.18097 which falls in the no decision area. We thus cannot accept H0 and cannot conclude that there is no autocorrelation amongst the data chosen. A more feasible estimation approach would be to have time series set out daily, weekly or monthly over 10 years rather than quarterly.

Ramsay test, no omitted variables testIn order to determine whether our specified model had misspecification, we tested for the functional form of GrowthR.

H0 : No mis-specification HA : Mis-specification

If P-value > α = 0.05 Reject H0

Since PStat = 0.3229 > 0.05 we accept H0 and conclude that the model has no mis-specification. Thus our model does not have any omitted variables.

Model 2 Conclusion Although we can conclude that Model 2 is unbiased as it satisfies the JB test, the Whites test and the VIF test, the model had no decision in terms of the Durbin Watson Test and thus we cannot conclude that the model is efficient. Overall the model is significant, however once again the only significant variable was ‘ConsumR’.


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8. Conclusion

In this study, an empirical analysis was initially undertaken to determine the impact that monetary policy has had on economic growth over the period of 2004-2014. This period is significant due to the Global Financial Crisis taking place in 2008. Whilst some countries felt the heavy weight of the Global Financial Crisis, Australia however emerged relatively sound. Model 1 looked to investigate whether monetary policy (changes in the cash rate), was a significant factor in cushioning the blow to Australia’s economic growth. The model used the three independent variables ‘RateR’ (Real Cash Rate), ‘ConsumR’ (Real Household Final Expenditure) and ‘InvestR’ (Final Private Business Investment). The R2 for the model was 0.5437, inferring that the independent variables only explained 54.37%. Using the t-test to test for the individual significance of the variables, we concluded that the only significant variable was ‘ConsumR’, which was significant at α = 0.01, α = 0.05, and α = 0.10. The F-test however concluded that the model overall was significant. The Model then went on to satisfy the JB test, the Whites test and the VIF test, allowing us to conclude that the model was unbiased. Final tests to confirm that there were no related errors and no omitted variables allowed us to determine that the model was efficient. Therefore we were able to conclude that the model was the best linear uniabsed estimator (BLUE). An analysis from these results has identified that ‘RateR’ is an insignificant variable in describing the percentage changes to GDP growth rates over the past decade. However if observations were collected and analysed over a longer period of time, then the significance of the variable ‘RateR’ could change. However broadening the data set over a larger timeline would reduce the data’s relevance to the global financial crisis. Therefore instead of extending the time-line for our data, we instead chose to input additional variables to attempt to further explain the changes in percentages growth of GDP over the past decade. The additional variables included ‘GovR’ (Final Government Expenditure), ‘NXR’ (Real Net Exports) and ‘EXGR’ (Exchange Rates AUD:USD). Although 3 new variables were introduced - two of which are components of the expenditure equation measuring GDP - Model 2 only emitted an R2 of 0.5815, an increase of only 3.78% from model 1. When tested for individual significance, once again ‘ConsumR’ was the only significant variable. According to the F-test, the model overall was significant. Although the model was concluded to be unbiased, no decision was found when testing for autocorrelation in the Durbin Watson test, therefore we could not conclude that it was efficient. Thus model 2 was not the best linear unbiased estimator (BLUE). After running an analysis on both Model 1 and 2, it can be said that based off the data collected on a quarterly basis over the period of 2004-2014, the only significant variable that explains the changes in GDP growth, was changes to household final expenditure. Based off findings in model 1, if household final expenditure increased by 1%, GDP would increase by 0.6528628%. Although the real cash rate was found not to be a significant factor in explaining changes to GDP growth over this period, economic theory tells us that decreases in the cash rate translate to decreases in lending rates, making it less expensive to borrow and own debt. Thus consumers are more likely to spend more, circulating more funds throughout the economy and increasing GDP growth. Therefore if a third model were to be investigated, we would


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examine the relationship between the cash rate and household final expenditure over the period of 2004-2014.


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9.ReferencesAlin, A. (2010). Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), pp.370-374.

Elias, S and Kulish, M (2010), “Direct Effects of Money on Aggregate Demand: Another Look at the Evidence”, Research Discussion Paper, Reserve Bank of Australia

Eder, A., Keiler, S. and Pichl, H. (n.d.). Interest Rate Risk and the Swiss Solvency Test. SSRN Journal.

Albright, C., Winston, W. and Zappe, C. ed., (2010). Data Analysis and Decision Making. 4th ed. Mason,America: Cengage Learning, p.616.

Cohen, B. (1996). Forecasting examples for business and economics using the SAS system. Cary, NC: SAS Institute.

Ajmani, V. (2008). Applied econometrics using the SAS system. Hoboken, NJ: Wiley.

Agalega, E & Antwi, S (2013), “The Impact of Macroeconomic Variables on Gross Domestic Product: Empirical Evidence from Ghana”, International Business Research, Vol. 6, No. 5, PP 108-116, http://www.ccsenet.org/journal/index.php/ibr/article/viewFile/26659/16285


http://www.ccsenet.org/journal/index.php/ibr/article/viewFile/26659/16285

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Appendix A


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Appendix B

Regress Model 1

JB test.


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Whites test.

VIF test.

Ramsey test.

DW test.


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Appendix C

Regress Model 2

JB test.


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Whites test.

VIF test.

Ramsey test.

DW test.


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AUSTRALIA AND THE GFC (Empirical Analysis) 19-5-2015

Economy & Finance

Transcript of AUSTRALIA AND THE GFC (Empirical Analysis) 19-5-2015