Statistical Analysis of Interrelationship between Money Supply Exchange Rates and Government Expenditure Evidence from Pakistan

A Statistical Analysis of Interrelationship between Money
Supply, Exchange Rates and Government Expenditure: Evidence
from Pakistan
Atif Ahmed
9549
atif_contact11@yahoo.com
Syed Muhammad Owais
9542
sm_live90@hotmail.com
Muhammad Asad Ali
9624
asad6281@gmail.com
Hafiz Wasif Kamal
9323
hafizwasif88@gmail.com
Muhammad Zakariya Qazi
9580
zakqazi@hotmail.co.uk
Research Method (Wed)
Submitted
To
Tehseen Jawaid
Fall Semester (2014)

2
Acknowledgements
First of all thanks to All Mighty Allah and then "We would
like to thank our teacher, Mr. Tehseen Jawaid for the
valuable advices and support which he has given to us on
writing of this report.
Regards

4
1. Introduction
Several researches have been conducted to study the impact of different macro-economic variables and
their influence on government expenditure. By using different statistical tools researchers have examined that
how money supply and exchange rate influence the government expenditure. Few other studies also conducted
work on the quarterly time series data to examine the long run equilibrium association between the
macroeconomic variables.
Friedman (1978), Blackely (1986) Ram (1988) suggested that the increase in government revenue plays a
vital role in increasing the government expenditure. Akhtar and Abbas (2002) drawn the conclusion that
exchange rates might differ due to decrease in government expenditure. Guffey (1985) concluded that money is
not neutral, because it is the monetary policy that heavily effects inflation which will again adversely affect the
economic growth. Studying over 100 countries it is suggested by Landau (1983) that growth rate and
government expenditure are negatively related. Grier and Tullock (1989) use pooled regression on five-year
averaged data in 113 countries to analyze the relationship between cross-country growth and various
macroeconomic variables. They find that the mean growth of government share of GDP generally has a negative
impact on economic growth. This finding implies that an increase in the government size as measured by a share
of government expenditures to GDP hampers economic growth. Barro (1990) also discovers the negative
0
10
20
30
40
50
60
70
80
90
100
Year
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
ExchangeRates
YEAR
Exchange Rate Value
Exchange Rate Value

5
relationship between the size of government and economic growth. Miller and Russek (1997) indicate that debt-
finance increases in government expenditure retarded growth.
It is evident from previous researches that money supply has an impact on government expenditure as
well as economic growth. By taking this hypothesis this research made an attempt to study these variables and
their relationship in Pakistan economy in time period of 1970 to 2012. Also it will investigate that what role is
played by exchange rate in government spending and will try to find out its nature.
0
10
20
30
40
50
60
Year
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Moneysupply
YEAR
Money and quasi money (M2) as % of GDP

7
2. Literature Review
2.1.Theoretical Background
The theoretical relationship between government expenditure and money supply and exchange rates is
very important to understand. According to monetarist theory it is assumed that changes in money supply exert a
dominant influence on changing patterns of government expenditure. The public’s demand for money is another
important part of the relationship between money supply and government expenditure1
.
This should be kept in mind that if Rs. 1 bought yesterday what Rs. 2 bought today then there will be a
need for Rs. 2 to sustain the purchasing pattern. That’s why the increased government expenditure is a false
increase in money supply because it is the deficit that government needs to finance, a reserve of money is there
for loans for the government from State Bank of Pakistan and other financial institutions. Now, the exchange rate
is also a major macroeconomic variable that if left ungoverned can be very threatening in fiscal policy making2
.
Literature that was analyzed for this research paper shows little or no sign of exchange rates impacting
government expenditure, which is why this research paper might throw some light on the nature of relationship
between exchange rates and government expenditure.
2.2.Empirical Studies
Sola and Peter (2013) examined the money supply and inflation rate in Nigerian economy. Variables
that were used are inflation rate, money supply, interest rate, exchange rate, oil revenue, and government
expenditure. Time series data has been used for the study purpose from 1970 to 2008. Econometric tests like
Vector Auto-Regression (VAR), Augmented Dickey-Fuller, and Granger Causality were used. A positive
relation between money supply and inflation rate is found also causality test shows unidirectional causality
between exchange rate and inflation rate, interest rate and inflation rate, also money supply and government
expenditures.
Ali et al (2013) enquired the impact of government expenditures on economic growth during different
democratic and dictatorial eras. The data is from the year of 1972-2009. Different sub-categories of government
1
Amedeo Strano (2002)
2
Cakrani et al (2013)

8
expenditures are taken as variables. Data analysis is carried out by using Auto-Regressive Distributed Lag
(ADL). It is concluded that development expenditure heavily support economic growth and it’s also validates the
assumption of that public and private investments are complementary to each other.
Georgantopoulos and Tsamis (2012) investigated the interrelationship of money supply, government
expenditure, prices, and economic growth in both short-run and long-run in Cyprus. For this purpose the variable
used are M2 (broad money), CPI, and Real GDP. Annual data is gathered form year 1980 to 2009. Error Correction
Model (ECM) and Johnson co-integration techniques were used to analyze the data. Findings show that inflation
effects economic growth very badly, and this is recommended to control government expenditures that increase
aggregate demand and shifting of these expenditures to increase aggregate supply.
Husnain et al (2011) questioned the interrelationship of government spending, foreign direct investment,
and economic growth in Pakistan scenario. The data is collect from 1975 to 2008. Four variables were taken for
analyses that are capital (K), labor (L), foreign direct investment (F), and total factor productivity (A). It is
concluded that one most contributing factor in Pakistan’s economic growth is trade openness and the existing
government expenditure structure is not in accordance to the economic needs it must be reorganized.
Mehmood and Sadiq (2010) examined the effect of government expenditure in the form of fiscal deficits on
level tax revenues. Using controlled variables poverty, government expenditure, remittance, private investment, and
secondary school enrollment from 1976 to 2010 annually. For long-run relationship co-integration test is applied and
for short-run error correction model is used. The Augmented Dickey-Fuller results show that poverty reduces due to
public spending thrift and remittances. Also public spending will increase aggregate demand in long-run.
Muhammad et al (2009) examined the relationship among government expenditure, M2, inflation and
economic growth in Pakistan. Data was collected from the year 1977 to 2007. The method used for this purpose was
Johnson co integration test to check the long term relationship, Granger causality test also has been used. The
research findings revealed that there exists a negative relationship b/w public expenditure and inflation with
economic growth. Also M2 is positively related to economic growth. The only reason that was determined for
negative relationship by the research was due to cost push inflation. The research suggested that growth rate of
money supply should be controlled. And governments must try to restrain from financing deficit by borrowing from
central bank.

9
Iqbal and Malik (2009) studied the relationship between government expenditure and taxes for the case of
Pakistan using annual data from 1961 to 2008. The method used Vector Error Correction Model (VECM), granger,
co-integration model, Augmented Dickey-Fuller and Johnson Co-integration approach. The analysis shows that in
case of Pakistan budget deficit and debt have close relationship. But the budget deficits have no impact on the
behavior of government taxes and expenditure in Pakistan. It means in case of Pakistan results found that taxes and
spending decision have no relation and there is no long run co-integration between taxes and expenditure.
Adrison (2002) explained money supply and government expenditure on economic growth in Indonesia
to check the asymmetric effect. Money supply and government expenditures are used as variables to run the
tests. The data collected is quarterly from 1980-1997. The technique that has been used is TARCH (Threshold
Auto Regressive Conditional Heteroscedasticity). The regression model showed that the government expenditure
has an asymmetric effect on economic output and also that money supply is not a contributing factor in inflation.
It is recommended that reallocation of government spending more towards developmental programs.
Albatel (2000) attempted to instigate the relationship between government expenditure and economic
growth in Saudi Arabia. GDP, government expenditure, government consumption, private investment, oil revenues,
and other revenues are variables upon which data is collected from 1964-1995. Method that is used to examine the
data is Augmented Dickey-Fuller (ADF). On the basis of results it is concluded that government should continue
providing economic infrastructure and social activities and on the other hand advise and encourage the private
investors to play a meaningful role as well.
Hsieh and Lai (1994) queried the relationship of government spending and economic growth in G-7
countries. The data is collected on Real GDP and share of government expenditure on goods and services from
1885-1997. Data analysis is done by using ADF and Phillip-PerronZt(q) test. The findings proved that relationship
between government spending and economic growth can vary across time. But still government spending is the only
best way possible for growth in economy.

11
3. Methodology
3.1.Modeling Framework
To estimate the effect of money supply (MS) and exchange rates (ER) on government
expenditure (GE) in Pakistan following model is used:
GE= α + β1MS +β2ER + ε
Hₒ1 = MS does not affect GE
Hₒ2 = ER does not affect GE
Where GE is the percentage of GDP of Gross National Expenditure is taken, MS is taken as Quasi
Money M2 as percentage of GDP and ER is taken as amount in rupees against 1 US dollar; Data for all
research variables is from the time period of 1970 to 2012. Data for research variables under
consideration is taken from World Bank3
.
3
Link address: http://data.worldbank.org/country/pakistan

13
4. Estimation and Result
4.1.1. Estimate Equation (Regression)
Hₒ= MS and ER does not affect GE
Table 1
Variables Coefficient Prob
Constant 124.0585 0.0000
MS -0.367716 0.0079
ER -0.058903 0.0045
It is shown that MS and ER have negative relation with the dependent variable GE, meaning if
MS increase by 1 unit GE will decrease by 0.3167 units and if ER increase by 1 unit than GE will
decrease by 0.0589 units.
4.1.2. Stationary Analysis
So to check the trend in the data stationary analysis is conducted for verification. Unit root test is
used for stationary analysis with Augmented Dickey-Fuller statistic. The hypothesis is that there is a trend
in data or the data is non-stationary.
Unit root test has been performed for all variables with level and 1st
difference, and 2nd
difference is also used where 1st
difference shows the contradiction. The test has been run for individual
intercept and individual intercept & Trend. The results have been reported in table 2.
Table 2
Variables
Level 1st
Difference
C C&T C C&T
GE -2.023248 -2.148364 -6.912705 -6.822305
MS -4.407745 -4.665224 -5.342827 -6.176806
ER 2.933613 -4.665224 -3.839290 -4.988724

14
4.1.3. Heteroskedasticity Test
Heteroskedasticity often arises in cross sectional data but the data taken for the research purpose
is already time series. The P value is 0.121 it means that there is no existence of heteroskedasticity in the
data.
4.1.4. Auto Correlation LM Test
When computed, the resulting number can range from 0 to 4. An autocorrelation of less than 2
represents perfect positive correlation, an increase seen in one time series will lead to a proportionate
increase in the other time series, while a value of greater than 2 represents perfect negative correlation, an
increase seen in one time series results in a proportionate decrease in the other time series4
.
The probability value is 0 it means there exist positive autocorrelation in the data. The removal
of auto correlation can be viewed in appendix.
4
Link address http://www.investopedia.com
Prob 0.121
Heteroscedesticity Test
Table 3
Prob 0.000
Table 4
Breusch-Godfrey Serial Correlation LMTest

15
4.1.5. Stability Test
CUSUM TEST CUSUM SQUARE TEST
CUSUM is the difference between each measurement and the
error value. We can see in the above diagram that the error limit
is ±5%. All the data variable variables are under the significance
level showing that data is stable.
To verify the previous CUSUM SQUARE test is carried out here
we can see that the provided data is crossing the significance
level of 5% somewhere in year 1997 or 1998 which is not
required so as to remove it we take the help of Chow’s breakpoint
test. (see appendix)
4.1.6. Co-integration test
Table 5
Variables
Hypothesis
No. of CE(s)
Trace
Statistics
5% Critical
Values
Maximum
Eigen Value
Statistics
5% Critical
Values
GE MS ER
None * 32.91231 29.79707 23.72303 21.13162
At most 1 9.189281 15.49471 6.234627 14.26460
At most 2 2.954653 3.841466 2.954653 3.841466
* denotes rejection of the hypothesis at the 0.05 level
It is found out that there is no co-integration between the said variables government expenditure, money
supply, and exchange rates. At level “None” the p-value appeared to be 0.0212 or 2.12% less than 5% significance
level. So we reject Hₒ that says variables are co-integrated.
4.1.7. Causality Analysis
Table 6
Null Hypothesis F-Statistics Probability
ER does not Granger Cause GE 0.43866 0.51167
GE does not Granger Cause ER 0.95095 0.33549
-20
-10
0
10
20
1975 1980 1985 1990 1995 2000 2005 2010
CUSUM 5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
1975 1980 1985 1990 1995 2000 2005 2010
CUSUM of Squares 5% Significance

16
MS does not Granger Cause GE 0.12182 0.72895
GE does not Granger Cause MS 0.82282 0.36993
MS does not Granger Cause ER 0.39045 0.53570
ER does not Granger Cause MS 0.00973 0.92194
It is evident from the causality test that money supply and exchange rates both combined are not
causing government expenditure. But, it is the government expenditure that is causing money supply and
exchange rates.
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Chapter 5
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5. Conclusion 
On the basis of data analysis the study is able to conclude that money supply and exchange rates are
negatively affecting government expenditure. Using the annual data of the period 1970-2012, we found that there is
negative relationship between money supply and exchange rates on government expenditure in long run.
First of all, we have to understand that change in money supply is not the only way of financing
government spending, but for past several years there has been a lot of pressure given on central bank borrowing,
plus borrowing from international financial institution have rapidly change Pakistan’s economic positions. Secondly
a country’s economic performance can be judged by its exchange rate values, one can realize the fact that the more
rupees depreciate then more money is required by the government to pay the debts and run the economic operations.
Whatever the reason is government must need to curtail its expenses and find some other solutions rather
than burdening it’s on economy. Some of the policy recommendation that government authorities need to consider
are a follow:
1. Government expenditure on infrastructure so as to facilitate the economic growth in the long-term scenario.
2. Developing such financial and capital markets that can mobilize savings and channel them to productive use.
3. Investment should be made in international arena in different assets with the aim of having high return rather
than borrowing from World Bank or other financial bodies.
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6. References
o Buchanan and Wagner 1977 “Democracy in Deficit”, New York: Academic Press
o Buchanan and Wagner 1978 “Dialogues concerning Fiscal Religion”, Journal of Monetary Economics, vol.4, pp
627-36
o Blackley 1986 “Causality between Revenues and Expenditures and the Size of the Federal Budget” Public Finance
Quarterly, vol.14, pp 139-56
o Ram 1988 “Additional Evidence on Causality between Government Revenue and Government Expenditure”,
Southern Economic Journal vol-54, pp 763-69
o Guffey 1985 “The Federal Reserve’s Role in Promoting Economic Growth”, FRB Kansas city, Economic Review
o Landau 1983 “Government Expenditure and Growth Rate: A Cross Country Study”, Southern Economic Journal,
vol-49, 783-92
o Iqbal an Malik “Budget Balance through Revenue or Spending Adjustment: Evidence from Pakistan”, The Pakistan
Development Review, 49:4 Part II (Winter 2010) pp. 611–630
o Jiranyakul 2007 “The Relation between Government Expenditures and Economic Growth in Thailand”, Munich
Personal RePEc Archive, Paper no 46070
o Mehmood and Sadiq 2010 “The Relationship between Government Expenditure and Poverty: A Cointegration
Analysis”, Romanian Journal of Fiscal Policy, Volume 1, Issue 1, July-December 2010, Pages 29-37
o Hsieh and Lai 1994 “Government spending and Economic Growth: The G-7 Experience”, California State
University, Journal of Applied economics, Vol 26, 535-542
o Adrison 2002 “The Effect of Money Supply and Government Expenditure Shock in Indonesia: Symmetric or
Asymmetric”, International Study Program
o Strano 2002 “How and How much can the money Supply affect the Inflation
Rate”,http://ukdataservice.ac.uk/media/263125/strano-paper.pdf
o Sola and Peter 2013 “Money Supply and Inflation in Nigeria: Implications for National Development”, Modern
Economy, http://www.scrip.org/journal/me, Vol 4, 161-170
o Cakrani et al 2013 “ Government spending and Real Exchange Rate: Case of Albania”, European Journal of
Sustainable Development, Vol 2, Issue 4, 303-310
o Albatel 2000 “The Relationship between Government Expenditure and Economic Growth in Saudi Arabia”,
journal of King Saud University, Vol 2, 173-191
o Georagantopoulas and Tsamis 2010 “The Interrelationship between Money Supply, Prices, Government
Expenditure, and Economic Growth: A Causality Analysis for the Case of Cyprus” International Journal of
Economic Science and Applied Research, Vol 5 (3), 115-128
o Mohammad et al 2009 “An Empirical Investigation between Money Supply, Government Expenditure, Output &
Prices: The Pakistan Evidence”, European Journal of Economics, Finance and Administrative Sciences ISSN 1450-
2275 Issue 17

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o Husnain et al “Public Spending, Foreign Direct Investment and Economic Growth”, International Research Journal
of Finance and Economics ISSN 1450-2887 Issue 61
o Ali et al 2013 “The composition of public expenditures and economic growth: evidence from
Pakistan", International Journal of Social Economics, Vol. 40 Issue 11, pp.1010 – 1022

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APPENDIX #1
Year Exchange Rate Value
Money and quasi money
(M2) as % of GDP
Gross national expenditure (% of GDP)
1970 4.76 43.46080808 106.9011662
1971 4.76 46.76431456 105.6476224
1972 8.68 51.30452441 105.2081712
1973 9.99 47.19722464 102.7768005
1974 9.9 35.90238629 107.096748
1975 9.9 33.66789516 111.5387994
1976 9.9 37.839395 108.652411
1977 9.9 39.35625433 109.7361804
1978 9.9 40.0350311 109.2384445
1979 9.9 43.11308147 112.0646621
1980 9.9 41.49679392 111.612632
1981 9.9 39.01479608 110.6951501
1982 11.85 40.79247985 111.8188964
1983 13.12 43.87686615 111.0590559
1984 14.05 39.85505175 111.5557487
1985 15.93 40.66128466 112.3908293
1986 16.65 43.30970533 110.7658504
1987 17.4 45.31106102 107.7687996
1988 18 41.36542167 108.0837644
1989 20.54 38.98095986 107.8638429
1990 21.71 39.13710282 107.8328811
1991 23.8 39.18989814 101.5607972
1992 25.08 42.74812397 103.169256
1993 28.11 45.65696029 106.134395
1994 30.57 45.75915506 102.7620197
1995 31.64 43.57084594 102.7128144
1996 36.08 46.04129003 104.5239233
1997 41.11 48.20323748 104.6883597
1998 45.05 47.1502314 101.0421428
1999 49.5 44.82025634 101.6129642
2000 53.65 38.59469838 101.2469581
2001 61.93 39.15125231 101.0524546
2002 59.72 43.25191223 100.0903955
2003 57.75 46.42524235 99.40656093
2004 58.26 48.36162239 98.9663297
2005 59.51 49.18651118 103.8742992
2006 60.27 44.55519367 107.413804
2007 60.74 47.43290591 106.5612017

24
2008 70.41 43.54570483 110.8295797
2009 81.71 40.27346075 107.2803434
2010 85.19 41.13943884 105.8363909
2011 86.34 37.47580384 105.0042579
2012 93.4 39.9150827 107.9296846

25
APPENDIX #2
Figure No 1. Figure No 2.
Figure No 3. Figure No 4.

26
Table 1: Regression
Dependent Variable: GE
Method: Least Squares
Date: 12/04/14 Time: 17:30
Sample: 1970 2012
Included observations: 43
Variable Coefficient Std. Error t-Statistic Prob.
ER -0.058903 0.019581 -3.008184 0.0045
MS -0.367716 0.131483 -2.796688 0.0079
C 124.0585 5.611903 22.10632 0.0000
R-squared 0.316909 Mean dependent var 106.3723
Adjusted R-squared 0.282755 S.D. dependent var 3.891667
S.E. of regression 3.295865 Akaike info criterion 5.290429
Sum squared resid 434.5090 Schwarz criterion 5.413303
Log likelihood -110.7442 F-statistic 9.278695
Durbin-Watson stat 0.593900 Prob(F-statistic) 0.000489
Table 2: Stationary analysis
GE variable
Level: Intercept
Null Hypothesis: GE has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.023248 0.2761
Test critical values: 1% level -3.596616
5% level -2.933158
10% level -2.604867
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(GE)
Date: 12/04/14 Time: 18:14
Sample (adjusted): 1971 2012
Included observations: 42 after adjustments
GE(-1) -0.187393 0.092620 -2.023248 0.0498
C 19.95092 9.855305 2.024384 0.0496

27
Log likelihood -94.12229 Hannan-Quinn criter. 4.607582
F-statistic 4.093532 Durbin-Watson stat 1.998466
Prob(F-statistic) 0.049766
Level: Trend and Intercept
Null Hypothesis: GE has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=1)
t-Statistic Prob.*
5% level -3.520787
10% level -3.191277
Dependent Variable: D(GE)
Date: 12/17/14 Time: 19:59
GE(-1) -0.226091 0.105239 -2.148364 0.0380
C 24.63691 11.55258 2.132590 0.0393
@TREND("1970") -0.026558 0.033723 -0.787544 0.4357
1st
Difference: Intercept
Null Hypothesis: D(GE) has a unit root
Exogenous: Constant
t-Statistic Prob.*

28
5% level -2.935001
10% level -2.605836
Dependent Variable: D(GE,2)
Date: 12/04/14 Time: 18:19
D(GE(-1)) -1.116067 0.161452 -6.912705 0.0000
C 0.050291 0.383336 0.131193 0.8963
1st
Difference: Trend and Intercept
Null Hypothesis: D(GE) has a unit root
t-Statistic Prob.*
5% level -3.523623
10% level -3.192902
Dependent Variable: D(GE,2)
Date: 12/17/14 Time: 20:00
D(GE(-1)) -1.115871 0.163562 -6.822305 0.0000
C -0.011621 0.819646 -0.014178 0.9888
@TREND("1970") 0.002815 0.032815 0.085771 0.9321

29
MS variable
Level: Intercept
Null Hypothesis: MS has a unit root
Exogenous: Constant
t-Statistic Prob.*
5% level -2.935001
10% level -2.605836
Dependent Variable: D(MS)
Date: 12/17/14 Time: 20:02
MS(-1) -0.552360 0.125316 -4.407745 0.0001
D(MS(-1)) 0.453209 0.145532 3.114156 0.0035
C 23.49588 5.380859 4.366567 0.0001
R-squared 0.355535 Mean dependent var -0.167054
Null Hypothesis: MS has a unit root
t-Statistic Prob.*

30
5% level -3.523623
10% level -3.192902
Dependent Variable: D(MS)
Date: 12/17/14 Time: 20:04
MS(-1) -0.597087 0.127987 -4.665224 0.0000
D(MS(-1)) 0.487622 0.145949 3.341049 0.0019
C 24.26573 5.346348 4.538748 0.0001
@TREND("1970") 0.052086 0.037651 1.383389 0.1748
1st
difference: Intercept
Null Hypothesis: D(MS) has a unit root
Exogenous: Constant
t-Statistic Prob.*
5% level -2.935001
10% level -2.605836
Dependent Variable: D(MS,2)
Date: 12/04/14 Time: 18:23
D(MS(-1)) -0.839546 0.157135 -5.342827 0.0000
C -0.143632 0.528907 -0.271564 0.7874

31
1st
Null Hypothesis: D(MS) has a unit root
t-Statistic Prob.*
5% level -3.526609
10% level -3.194611
Dependent Variable: D(MS,2)
Date: 12/17/14 Time: 20:05
D(MS(-1)) -1.191044 0.192825 -6.176806 0.0000
D(MS(-1),2) 0.393181 0.151120 2.601788 0.0134
C -0.772192 1.092379 -0.706890 0.4842
@TREND("1970") 0.022737 0.043197 0.526346 0.6019
ER variable
Level: Intercept
Null Hypothesis: ER has a unit root
Exogenous: Constant
t-Statistic Prob.*

32
Augmented Dickey-Fuller test statistic 2.933613 1.0000
5% level -2.933158
10% level -2.604867
Dependent Variable: D(ER)
Date: 12/05/14 Time: 07:40
ER(-1) 0.048655 0.016585 2.933613 0.0055
C 0.531435 0.673102 0.789531 0.4345
Null Hypothesis: ER has a unit root
t-Statistic Prob.*
5% level -3.520787
10% level -3.191277
Dependent Variable: D(ER)
Date: 12/17/14 Time: 20:08

33
ER(-1) -0.013444 0.051989 -0.258601 0.7973
C -0.283093 0.930000 -0.304401 0.7624
@TREND("1970") 0.131623 0.104522 1.259287 0.2154
1st
Difference: Intercept
Null Hypothesis: D(ER) has a unit root
Exogenous: Constant
t-Statistic Prob.*
5% level -2.935001
10% level -2.605836
Dependent Variable: D(ER,2)
Date: 12/05/14 Time: 07:42
D(ER(-1)) -0.580057 0.151084 -3.839290 0.0004
C 1.326366 0.511912 2.591006 0.0134

34
1st
Null Hypothesis: D(ER) has a unit root
t-Statistic Prob.*
5% level -3.526609
10% level -3.194611
Dependent Variable: D(ER,2)
Date: 12/17/14 Time: 20:10
D(ER(-1)) -0.930109 0.186442 -4.988724 0.0000
D(ER(-1),2) 0.303859 0.160752 1.890238 0.0668
C -0.683185 0.832711 -0.820435 0.4174
@TREND("1970") 0.117774 0.037899 3.107564 0.0037
Table 3
Heteroscedesticity Test
White Heteroskedasticity Test:
F-statistic 1.884473 Probability 0.120651
Obs*R-squared 8.727726 Probability 0.120430
Test Equation:
Dependent Variable: RESID^2
Date: 12/04/14 Time: 17:35
Sample: 1970 2012
Included observations: 43

35
C -228.0955 168.4667 -1.353950 0.1840
ER 0.810815 1.084077 0.747931 0.4592
ER^2 -0.005374 0.003680 -1.460195 0.1527
ER*MS -0.005402 0.020903 -0.258439 0.7975
MS 11.18316 7.991880 1.399315 0.1700
MS^2 -0.135581 0.094344 -1.437096 0.1591
Table 4
Auto-Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test:
Obs*R-squared 21.16547 Probability 0.000004
Test Equation:
Dependent Variable: RESID
Date: 12/04/14 Time: 17:37
Presample missing value lagged residuals set to zero.
ER 0.003985 0.014146 0.281704 0.7797
MS 0.060006 0.095387 0.629077 0.5330
C -2.627672 4.072397 -0.645240 0.5226
RESID(-1) 0.720410 0.117167 6.148574 0.0000
R-squared 0.492220 Mean dependent var -7.44E-15

36
Chow Breakpoint Test
Chow Breakpoint Test: 1998
Log likelihood ratio 16.32138 Probability 0.000974
Chow Breakpoint Test: 1999
Log likelihood ratio 16.22952 Probability 0.001017
Table 5
Co-integration Test
Date: 12/17/14 Time: 21:19
Trend assumption: Linear deterministic trend
Series: GE MS ER
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.439323 32.91231 29.79707 0.0212
At most 1 0.141067 9.189281 15.49471 0.3482
At most 2 0.069529 2.954653 3.841466 0.0856
Trace test indicates 1 cointegratingeqn(s) at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.439323 23.72303 21.13162 0.0211
At most 1 0.141067 6.234627 14.26460 0.5833
At most 2 0.069529 2.954653 3.841466 0.0856
Max-eigenvalue test indicates 1 cointegratingeqn(s) at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
GE MS ER
0.117424 0.323591 0.006315

37
-0.170243 -0.040172 0.025722
-0.279652 -0.009059 -0.047993
Unrestricted Adjustment Coefficients (alpha):
D(GE) 0.402600 0.686293 0.254698
D(MS) -2.033177 0.279529 -0.233012
D(ER) 0.686581 0.429384 -0.537399
1 Cointegrating Equation(s): Log likelihood -280.1015
Normalized cointegrating coefficients (standard error in parentheses)
GE MS ER
1.000000 2.755755 0.053777
(0.47528) (0.07544)
Adjustment coefficients (standard error in parentheses)
D(GE) 0.047275
(0.04142)
D(MS) -0.238743
(0.05032)
D(ER) 0.080621
(0.04819)
2 Cointegrating Equation(s): Log likelihood -276.9842
Normalized cointegrating coefficients (standard error in parentheses)
GE MS ER
1.000000 0.000000 -0.170274
(0.12266)
0.000000 1.000000 0.081303
(0.05334)
Adjustment coefficients (standard error in parentheses)
D(GE) -0.069561 0.102708
(0.06900) (0.10880)
D(MS) -0.286331 -0.669147
(0.08810) (0.13890)
D(ER) 0.007521 0.204922
(0.08357) (0.13176)

38
Table 6
Granger’s Causality Test
Pairwise Granger Causality Tests
Date: 12/04/14 Time: 17:56
Sample: 1970 2012
Lags: 1
Null Hypothesis: Obs F-Statistic Probability
ER does not Granger Cause GE 42 0.43866 0.51167
GE does not Granger Cause ER 0.95095 0.33549
MS does not Granger Cause GE 42 0.12182 0.72895
GE does not Granger Cause MS 0.82282 0.36993
MS does not Granger Cause ER 42 0.39045 0.53570
ER does not Granger Cause MS 0.00973 0.92194

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