The effects of fiscal policy on economic growth: empirical evidences based on time series data from Pakistan.
Ali, Shahid ; Ahmad, Naved
This study investigates the effectiveness of fiscal policy and its
impact on macroeconomic activities in Pakistan during the period
1972-2008. It examines the role of fiscal policy under democratic and
military regimes. Recognising the shortcomings of traditional
procedures, this study adopts modern econometric techniques to identify
the effects of fiscal policy on macroeconomic activities. The
sensitivity analysis procedure has been carried out to select the robust
variables, which are not sensitive to different econometric techniques.
Dynamic simulations have also been performed to observe the reaction of
unexpected structural and policy shocks. Using Auto Regressive
Distribute Lag (ARDL) model, we find that the overall fiscal deficit
exerts a negative effect on economic growth in the long run. Hence, this
study concludes that expansionary fiscal contraction occurs in Pakistan.
In order to estimate short-run dynamics, we use the Error Correction
Mechanism (ECM). In the short run, the overall fiscal deficit has a
significant impact on economic growth. The study recommends that the
budget deficit should be in the narrow band of 3 to 4 percent of GDP.
Beyond this limit, the unsustainable budget deficit could have
undesirable macroeconomic costs and the government's macroeconomic
objectives such as low inflation and high economic growth might be in
jeopardy. The government should also curtail nonproductive expenditures.
Particular attention should be given to the Public Sector Development
Plan (PSDP), as it is the complementary of private investment and has a
longterm impact on macroeconomic activities.
JEL classification: E62, O4 and C32
Keywords: Fiscal Policy, Economic Growth, ARDL
1. INTRODUCTION
Fiscal policy refers to government's efforts to influence the
direction of the economy through changes in taxes or expenditures.
Optimal fiscal policy in Pakistan and in other developing countries
plays a pivotal role in growth process and, hence, serves as a vital
instrument for economic growth. The efficacy of fiscal policy in
improving economic conditions in the long run is, however, a
controversial issue and needs further investigation.
In conventional model, a federal tax cut without a corresponding
reduction in federal expenditures will encourage consumption
expenditures and interest earnings due to increase in personal
disposable income. Contrarily, according to Ricardian Equivalence Theorem (RET), the same change in fiscal policy will not result in any
of the above mentioned macroeconomic impacts. In other words, a
reduction in deficit-financed federal tax cut will not affect
macroeconomic outcomes [Saxton (1999)].
The empirical literature on the effects of fiscal policy on
Pakistan's economic growth is still at its infancy, we surmise.
Shabbir and Mahmood (1992), Iqbal (1995. 1994, 1998), Khilji and Mahmood
(1997) have concluded that fiscal deficit is one of the significant
variables that affects economic growth in Pakistan. Haq (2003), on the
other hand, has argued that fiscal deficits do not have any effect on
key macroeconomic indicators such as investment, inflation and GDP
growth. The impact of fiscal policy on economic growth can also be
demonstrated and explored through transmission mechanism; it affects
economic growth via demand and supply sides. According to Khalid, et al.
(2008) fiscal policy is considered to have dynamic transmission
mechanism, as it carries longer policy lags for different macroeconomic
variables and hence, it has different impacts on key macroeconomic
variables.
Recognising the importance of sound fiscal policy, the present
study explores the link between fiscal policy and economic growth for
Pakistan's economy for the period 1972-2008. The study also
examines the effectiveness of fiscal policy in different political
regimes. Using dynamic model and various econometric techniques, this
study tests the significance of various empirical models. The study also
imparts some policy recommendations for the development of sound fiscal
policy in Pakistan. This study is the first empirical analysis on the
"effectiveness of fiscal policy and its impact on economic
growth" in Pakistan.
The rest of this study is organised as follow: Section 2 presents
the summary of review of literature on the effects of fiscal policy on
economic growth in different parts of the world. Section 3 presents the
model specification and methodology. Section 4 represents the empirical
findings and the last section provides concluding remarks and policy
implications.
2. LITERATURE REVIEW
"The macroeconomic relationship between fiscal policy and
economic growth has long fascinated economists. Unfortunately, analyses
of that relationship have frustrated empiricists for almost as long. One
root of that frustration is the array of possible policy
indicators" [Fu, et al. (2003)].
A large number of studies have been carried out to examine the
impact of fiscal policy variables on economic growth, investment,
consumption, inflation, exchange rate, external deficit and other
macroeconomic activities [Landau (1986); Huoppner (2003); Perotti
(2005), Amanja and Morrissey (2005); Falk, et al. (2006); Rezk (2006);
Castro, et al. (2006); Fatas and Mihov (1998); Sinha (1998); William and
Orszag (2003); Claus, et al. (2006) and Kukk (2006)]. Government
spending, tax revenues and budget deficits as fiscal policy variables
have been used by these authors and found different responses of
macroeconomic activities to fiscal innovations. According to Huoppner
(2003), Claus, et al. (2006), Esau (2006), Heppke-Falk, et al. (2006)
and Castro, et al. (2006), shocks to government spending positively
affect GDP growth rate, whereas shocks to taxes inversely affect GDP
growth rate. Furthermore, GDP growth rate responds negatively to budget
deficit in the long run [Balassa (1988); Iqbal and Zahid (1998); Jafri,
et al. (2006)]. Many researchers [Barro and Sala-i-Martin (1995);
Sala-i-Martin (1997); Mendoza, et al. (1997); Tanzi and Zee (1997);
Kneller and Gemmell (1999); Odedokun (2001); and Bose, et al. (2003);
Amanja and Morrissey (2005); Romero de Avila and Strauch (2007)] have
used fiscal policy variables in the growth equations and have found
their significant contribution. The rising budget deficit has been
considered as one of the main constraints to economic growth [Iqbal and
Zahid (1998); Fischer (1993); Easterly and Rebelo (1992); Levine and
Zervos (1993); Barro (1991); Mwebaze (2002) and Balassa (1988)]. From
the relevant literature it is clear that fiscal policy affects economic
growth. However, the sign and magnitude of the effects of different
tools of fiscal policy are ambiguous.
Only few studies have examined the effects of fiscal policy on
specific macroeconomic variables in Pakistan [Ahmad and Qayyum (2008);
Haque and Montiel (1991); Khalid, et al. (2008)]. Few studies have
included budget deficit in growth equations and have found that budget
deficit is one the significant variables affecting economic growth
[Shabbir and Mahmood (1992); Iqbal (1994, 1995, 1998); Khilji and
Mahmood (1997)]. As far as theoretical work regarding the relationship
between fiscal policy and economic growth is concerned, the most notable
work has been done by Trevala (2005) and Blinder and Solow (1972).
Tervala (2005) argued that fiscal growth raises the output of non traded
goods and crowds out private consumption of non traded goods. However,
Blinder and Solow (1972) argued that in the simplified IS-LM framework
the long run sign of the pure fiscal multiplier is undermined a priori,
fiscal policy only acts perversely in unstable system.
3. MODEL SPECIFICATION, DATA AND METHODOLOGY
3.1. Model Specification and Data
In order to examine the effects of fiscal policy on economic
growth, we estimate the following equation.
[Y.sub.t] [[lambda].sub.0] + [[lambda].sub.1] F[P.sub.t] +
[[lambda].sub.2] [X.sub.t] +[[lambda].sub.3] [(FP*DUM).sub.t] ... (1)
Where Y = Growth rate of GDP per capita, vector X represents the
set of control variables i.e., private investment (PINV), inflation
(INF), current account deficit (CAD) and FP represents Fiscal Policy
variables. In the above equation changes in FP variables has a dynamic
impact on Y. Further, to capture the effects of fiscal policy in
democratic and military regimes, we include the interaction term of
fiscal policy with political dummy.
We use overall fiscal deficit as a proxy of fiscal policy.
The data for this study consist of annual observations for the
period 1972-2008. The most important data source is Economic Survey of
Pakistan (Government of Pakistan). A multivariate framework is employed
in this study. (1)
3.2. Methodology
This study concentrates on the ADF and PP and Ng-Perron unit root
tests. To test the long run relationship, this study uses the robust
econometric technique, Autoregressive Distributed Lag model (ARDL),
popularised by Pesaran and Shin (1998), and Pesaran, et al. (2001).
The error correction version of ARDL model is given below for the
above given Equation (1).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... (2)
Where Y represents real GDP growth rate, FP represent fiscal policy
variables such as fiscal deficit as a percent of GDP (FD), current
expenditures as a percent of total expenditures (CE) and development
expenditures as a percent of total expenditures (DE). X represents
control variables. [[beta].sub.0] is drift component and [mu] is white
noise.
In order to find out the short run coefficients, we use the
following equation:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... (3)
[eta] is the error correction term in the model indicates the pace
of adjustment reverse to long run equilibrium following a short run
shock.
Private investment is measured by the sum of business fixed
investment, residential investment and inventory investment. Moreover,
current account balance is measured by the sum of net exports of goods
and services, net income from abroad (Net Factor Payment) and net
unilateral transfers.
Samudram and Vaithilingam (2009) in case of Malaysia and Mohammadi,
et al. (2008) in case of Turkey used Autoregressive Distributed Lag
model (ARDL) to examine the impact of public expenditure on economic
growth.
To cope up with the endogeneity of explanatory variables, and to
avoid inconsistent results, this study uses two-stage least Square
(2SLS) instrumental variable techniques.
4. EMPIRICAL RESULTS OF GROWTH EQUATION
4.1. Testing of the Unit Root Hypothesis
ADF test, PP test and Ng-Perron unit root test were applied in
order to test the unit root hypothesis to all variables. A summary of
these test results is reported in Table 1.
Results show that each of the variables is integrated of different
order. The results of the unit root tests enable us to apply any
cointegration technique. The results of ADF and PP unit root tests show
that all variables are integrated of order one except PINV and CAD. The
results of Ng-Perron unit toot test show that all variables are
integrated of order one except CAD. The results of Ng-Perron unit root
test are given in Table 2.
To choose a robust model for estimation of growth equation, we
estimate different growth equations and select three of them for
comparison. These equations have been estimated via ARDL co-integration
technique.
4.2. Lag Selection of ARDL
After finding integrating order of all variable, the ARDL
co-integration system is implemented for Pakistan utilising annual data
over the period 1972-2008. In the first stage, the order of lag length
is usually obtained from unrestricted vector autoregressive (VAR) via
Schwartz Bayesian Criteria (SBC) and Akaike Information Criteria (AIC).
The order of lag length is "2" which is selected through the
minimum value of SBC as shown in Table 3.
Therefore, lag order 2 is selected on lowest value of SBC in Table
3 for the growth equation. In the next step, we determine individual lag
order for the estimation of ARDL, which is (2, 2, 2, 2, and 0). Finally,
the F-test Statistics is estimated on the basis of Wald-test. The
results are given in the following Table 4.
The results of bound testing approach show that calculated F
statistics is statistically significant for growth equation and higher
than upper bound critical value at 5 percent level of significance
implying that there is a co-integration among the variables in the
models. The stability of long run relationship among the variables in
the model is also clear from the cumulative sum (CUSUM) stability test.
(2) Having found a long run relationship, we apply the ARDL method to
estimate the long run and short run coefficients. (3) Long run results
are shown in Table 5.
We use fiscal deficit as a percent of GDP (FD), Private investment
as a percent of GDP (PINV), inflation rate (INF), current account
deficit as a percent of GDP (CAD) as explanatory variables in growth
equation. An interaction term of fiscal deficit with dummy of democracy
is also included in the growth equation.
ARDL technique provides best results in the presence of
endogeneity. (5) The explanatory variables and their lags are used as
instruments. It is clear from Table 5 that all variables have expected
signs and parameters are significant. The long run results suggest that
all variables are important factors affecting economic growth. The
coefficient of fiscal deficit is negative and significant at 1 percent
level of significance indicating that expansionary fiscal contraction
occurs in Pakistan. In the long run rising fiscal deficit reduces
national savings and slows down economic growth. These results support
the findings of other studies, which evidenced that fiscal deficit
negatively affects economic growth [Balassa (1988); Barro (1991);
Easterly and Rebelo (1992); Levine and Zervos (1993); Fischer (1993);
Barro and Sala-i-Martin (1995); Mendoza, et al. (1997); Tanzi and Zee
(1997); Kneller and Gemmell (1999); Odedokun (2001); Mwebaze (2002);
Bose, et al. (2003); Ali (2005); Amanja and Morrissey (2005); Jafari, et
al. (2006); Kukk (2006); Romero de Avila and Strauch (2007)]. The
results of this study also support the findings of the studies in
Pakistan [Shabbir and Mahmood (1992); Iqbal (1994, 1995); Khilji and
Mahmood (1997); Iqbal and Zahid (1998)]. The main reason of expansionary
fiscal contraction in Pakistan is that government activities are mostly
politically motivated and unproductive and therefore restrains growth.
Moreover, the huge fiscal deficit is due to non development
expenditures. Only interest payment of public debt and defence
expenditures exceed the development expenditures. Due to these reasons
fiscal deficit negatively affects economic growth in the long run. The
coefficient of Private investment is significant and its positive sign
indicates that high level of investment increases the productivity and,
hence, accelerates economic growth. The results show that inflation
negatively affects economic growth. This is due to the fact that
inflation decreases domestic demand and increases the cost of
production. These factors decelerate economic growth. Another important
inference drawn from the above result is that the sign of interaction
term is negative and significant indicating that fiscal deficit is
negatively affecting economic growth in military regime. The sign of
current account balance is negative and significant at 1 percent level
of significance; it indicates that an increase in current account
deficit decreases the foreign exchange reserves with host country and
hence, reduces economic growth. The coefficient of fiscal deficit is
positive when the square term of fiscal deficit is introduced in the
model. The square term with negative coefficient is the indication of
fiscal deficit Laffer curve in case of Pakistan. It means that fiscal
deficit is not a problem up to some threshold level.
The estimated lagged error correction term EC (-1) is negative and
highly significant. The negative and significant error correction term
also indicates that there is a long run relationship among the variables
Y, FD, PINV, INF and CAD. The feedback coefficient is -0.43. It suggests
that about 43 percent disequilibrium is corrected in the current year.
The result also suggests that in the short run fiscal deficit has
significant impact on economic growth. In the short run, increase in
fiscal deficit leads to a decrease in the real gross domestic product.
However, in the short run changes in CAD and [DELTA]FD*DUM have
insignificant impact on economic growth.
4.3. Sensitivity Analysis
Even though we have given the model specification, yet for the
purpose of estimation, we conduct sensitivity analysis and use only
robust variables, which are not sensitive to different econometric
techniques. For this purpose, we run a lot of regressions and choose the
most robust variables for our analysis. The robustness of the variables
is also apparent from the short run diagnostic test. From the results of
the short run diagnostic tests it is clear that there is no serial
correlation and hetroscedasticity in the model. To detect the problem of
autocorrelation and hetroscedasticty, we use serial correlation
Lagrangian Multiplier (LM) and autoregressive conditional
heteroskedasticity tests respectively. In order to test the normality of
error term, we use Jarque-Bera test. From the calculated value of Ramsey
RESET test it is clear that the functional forms of the models are
correctly specified. Moreover, the data is normally distributed. In
order to analyse the stability of long run and short run coefficients,
the CUSUM and CUSUMsq stability test are applied. The results of CUSUM
and CUSUMsquare show that all variables are cointegrated. Moreover, the
results show that neither the CUSUM nor the CUSUMsq test statistics
exceed the critical values, which ensure that all models are stable and
correctly specified. Furthermore, the robustness of the variables is
also apparent from the constancy of parameters by using both ARDL and
2SLS econometric techniques. The models are not sensitive to changes in
econometric techniques.
5. CONCLUSIONS AND POLICY IMPLICATION
In this study we examine the dynamic effects of fiscal policy on
macroeconomic activities over the period 1972-2008. ADF test, PP test
and Ng Perron unit root test are applied to test the unit root
hypothesis to all variables. The results of ADF and PP unit root tests
show that all variables are integrated of order one except CAD and PINV.
The results of Ng-Perron unit root test show that all variables are
integrated of order one except CAD. The results of the unit root tests
enable us to apply ARDL co integration techniques.
Using modern econometric approaches, the results show that there is
a long run relationship between overall fiscal deficit and economic
growth. It is clear from growth equation that all variables are
important factors affecting economic growth. The negative and
significant coefficient of fiscal deficit indicates that expansionary
fiscal contraction occurs in Pakistan. The main reason of expansionary
fiscal contraction in Pakistan is that government activities are mostly
politically motivated and unproductive and therefore restrains growth.
The huge fiscal deficit is due to non development expenditures.
Using the non linear equation, we find that fiscal deficit
positively affects economic growth up to some threshold level. Beyond
that threshold level, fiscal deficit negatively affects economic growth
and has some serious macroeconomic consequences.
For short run dynamics. Error Correction Mechanism (ECM) has been
used. The results of ECM suggest that in the short run overall fiscal
deficit exert significant impact on economic growth. This reveals the
fact that in the short run rising fiscal deficit creates excess demand,
which encourages firms to use more of their existing capacity and people
to spend more, and hence economic situation in the short run improves,
but in the long run rising fiscal deficit has some serious implication
for economic growth. The feed back coefficient is negative and
significant suggesting that about 0.43 percent disequilibrium in the
previous period is corrected in current year.
The study recommends that the government should keep its budget
deficit in the narrow band of 3 to 4 percent of GDP. Beyond this limit
the unsustainable budget deficit could have undesirable macroeconomic
costs and the government's macroeconomic objectives such as low
inflation and high economic growth might be in jeopardy. If the
government is able to reduce its budget deficit, eventually she would
get rid of the vicious circle of debt overhanging problem, because the
debt-GDP ratio would increase only if the fiscal deficit as a percentage
of GDP exceeds the real GDP growth rate. However, the reduction in
fiscal deficit must be due to reduction in the public expenditure rather
than an increase in resource mobilisation. The government should curtail
non productive expenditures; high attention should also be given to the
Public Sector Development Plan (PSDP), as it has a long term impact on
economic growth.
Appendices
APPENDIX 1
DEFINITION OF THE VARIABLES
The definitions of all variables (explanatory variables and
instrumental variables) used in this study are given below.
Overall Budget Deficit/Surplus = (Current Account Expenditures +
Development Expenditures) - (Repayment of Foreign Debt) - (Net Revenue
Receipts) - (the contribution by autonomous bodies) - (The amount earned
by disinvestment of shares).
Economic Growth = Growth rate in Real Gross Domestic Product (GDP)
Gross Private Domestic Investment = (Business Fixed Investment +
Residential Investment) + (Inventory Investment)
Current Account Balance = Net Exports of Goods and Services + Net
Income from abroad (NFP) + Net Unilateral Transfers
Inflation = Consumer Price Index (Inflation rate)
Public Debt = Total public debt as a percent of GDP.
Exchange Rate = Real exchange rate
Interest Rate = 6 months T- bill rate for short run and 9 months
T-bill rate for long run.
Money Supply = M1 + Saving Deposits including MMDAs (Money Market
Deposit Accounts) + Small Denomination time Deposits + MMMFs (Money
Market Mutual Funds).
APPENDIX 2
EMPIRICAL RESULTS USING 2SLS
Table 1
Estimated Coefficients Using 2SLS Techniques
Dependent Variable 2SLS
Real GDP Growth Rate (Y) Technique (6)
Regressors Coefficients
FD -1.11 **
PINV 0.21
INF -0.03 ***
CAD -0.69 ***
[FD.sup.2] --
FD *DUM -0.12 **
[R.sup.2] = 0.97
Adjusted [R.sup.2] = 0.96
F-statistics = 1532.06
Dh Stat = 1.86
Note: *, ** and*** represent Significant at 1 percent,
5 percent and 10 percent level of significance.
APPENDIX 3
RESULTS OF CUSUM AND [CUSUM.sub.SQ] (7)
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
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Department of Economics, Discussion Papers, ISSN 1459-3696. (Discussion
Paper No. 619).
William, G. Gale and Peter R. Orszag (2003) Economic Effects of
Sustained Budget Deficits. National Tax Journal 56:3.
Comments
Fiscal policy is now considered as a staunch option for
macroeconomic stabilisation programmes across many countries.
Considering the importance of the subject the authors have investigated
the effectiveness of fiscal policy measured as Budget deficit and
government consumption/investment expenditures interms of its effect on
macroeconomic activities in Pakistan by applying modern econometric
techniques. Further they have done the same by identifying different
governance regimes also.
My first comment is that most economist are using the ARDL
methodology, so it may not be called new, and further in econometrics
it's the suitability rather then complexity and time reference for
choosing the econometric technique.
Secondly the paper has a very broad objective and that too without
any theoretical/behavioural equations and that creates a problem of
where to start and where to end. e.g. had it just been contained on the
aggregate economy and identified some theoretical channels then it would
have been easy for the flow and conclusion of the paper. That is why we
see the crowding out, expansionary fiscal contraction, debt
sustainability, inflation, twin deficit etc. and so many other issues
taken care off in this paper.
Thirdly the paper must have been prepared in haste as it contains
many typographic mistakes referring to it being taken from a larger
document (may be a thesis).
Fourth it has so many issues contained in it that it confuses the
reader and to some extent the author as well, as there are contradictory
statements.
Further in the end it has policy implications which do not emerge
from the paper, e.g. proposing a threshold level of FD to GDP is not
estimated some where.
Specifically, in my opinion using just the criteria of SBC may not
be enough as there are other methods such as akaike and modeified form
of SBC and akaike which can be used to verify, above all the sheer
justification based on parsimonious model is also required for a result
to be true.
I hope the authors have adjusted the GDP after its rebasing in
2000, similarly the private consumption expenditures also need to be
adjusted.
Now I come to the most crucial part, the results, since the paper
does not theoretically identify the channels hence the justifications of
the results are all spontaneous and ad-hoc, without referring to peer
results or to a theory.
e.g. in the first model where growth in GDP is related to certain
factor, it is reported that in the long run inflation effects the output
growth negatively as it decreases aggregate demand and increases cost of
production. But with reference to table 63 (inflation); with money
supply increase inflation increases which stimulates the private
investment and hence increases the supply to cover for the increase in
aggregate demand and hence prices reduce. So there are conflicting
strong result and need referencing to some peers or to a theory.
In Equation 6.1.1 the fiscal deficit is shown to impact the private
investment negatively, as the access to credit for private borrower
reduces which in case of Pakistan is proved to be not holding by many
authors.
Further results such as pertaining to Table 21 we see that with
appreciation of exchange rate exports decrease and the private
investment increases; meaning as Dr Nadeem UI Haque would say domestic
commerce has increased.
In the VAR, although referred to the SVAR but the author has used
the Choleski decomposition which is arbitrary and highly dependent on
the ordering of the variable, therefore the variance decomposition and
impulse can be erroneous.
In the end I would still congratulate the author to bring upon a
good topic for discussion, but also encourage them to improve it in the
light of above comments.
Mahmood Khalid
Pakistan Institute of Development Economics, Islamabad.
(1) See Appendix 1 for the definitions of variables.
(2) The results of CUSUM are given in Appendix 3.
(3) For details see Pesran, et al. (2001).
(4) ARDL order is (2, 2, 2, 2, 0) selected based on SBC.
(5) To check the robustness of the model, we provide the results of
2SLS in Appendix 2. From the results of both techniques (ARDL and 2SLS)
it is clear that the parameters of the model are not sensitive to change
in econometric technique and hence, it shows the robustness of the
model.
(6) INT, M2, ER, PD and all of the variables in the growth equation
that are believed to be uncorrelated with the disturbances are used as
instrumental variables.
(7) The straight lines represent critical bounds at 5 percent
significance level.
Shahid Ali <
[email protected]> is Lecturer in Economics,
University of Swat, Khyber Pakhtunkhwa. Naved Ahmad
<
[email protected]> is Associate Professor and Chairman,
Department of Economics and Finance, Institute of Business
Administration (IBA), Karachi.
Authors' Note: We are thankful to Syed Akbar Zaidi, Haroon
Jamal and Khalid Mahmood for their helpful comments and suggestions. We
are, however, responsible for the views expressed in this paper and any
remaining errors.
Table 1
Unit Root results
ADF (Drift and Trend) P-P (Drift and Trend)
Variables Level 1st Diff Level 1st Diff
Y -0.56 -4.24 ** -0.09 -4.87 *
FD -1.84 -7.91 * -1.84 -7.91
PINV -6.06 * -3.92 *** -1.41 -10.30 *
PCON -1.52 -X1.88 * -1.61 -7.23 *
INF -0.72 -4.67 ** -1.41 -4.89 *
CAD -4.14 * -6.67 * -5.98 * 13.09 *
Notes: *(**) Shows significance at 1 percent (5 percent) level.
Table 2
Ng-Perron Unit Root Results
Ng-Perron Test Statistics
At Level
MZa MZt MSB MPT
Y -0.62 -0.23 0.37 37.15
FD -36.0 -134.29 0.01 0.01
PINY -9.69 -2.12 0.21 9.71
PCON -1.79 -0.49 0.27 24.08
INF -1.86 -0.84 0.45 40.55
CAD -17.96 ** -2.99 0.16 5.07
At 1st
Difference
MZa MZt MSB MPT
Y -17.61 * -2.96 0.16 5.19
FD -3.76 * -39.11 10.37 3.10
PINV -12.13 *** -2.46 0.20 7.51
PCON -15.03 *** -2.719 0.18 6.18
INF -55.82 * -4.80 0.08 3.77
CAD -13.39 ** -2.58 0.19 6.80
Notes: *(**) Shows significance
at 1 percent (5 percent) level.
Table 3
Lags Defined through VAR-SBC for Overall Model
Lag Selected through VAR-SBC Lag
Growth Equation
0 104.69
1 91.73
2 90.33 *
3 90.55
Notes: * Indicates minimum Schwarz SBC at the corresponding lag.
Table 4
Lag Length Selection and Bound Testing for Cointegration
Modal 1 (Growth Equation)
Order of the lags AIC HQ SBC F-test
Statistics
K = 1 116.65 118.57 117.28 2.31
K = 2 113.98 * 117.55 * 115.17 * 5.75 **
Short run Diagnostic Tests
Serial Correlation LM tests = 1.65 (0.32)
ARCH Tests: 1.54 (0.24)
White Hetroscedasticity Test: 0.76 (0.34)
Ramsey RESET = 1.02 (0.87)
Jarque-Bera Tests= 897.45 (0.00)
*(**) Significant at 10 percent (5 percent) level of significant
according to Pesaran, et al. (2001) and Narayan (2005).
Table 5
Estimated Long Run Coefficients Using the ARDL (4)
Dependent Variable ARDL Technique
Real GDP Growth Rate (Y) Order (2, 2, 2, 2, 0)
Regessors
Coefficients
LFD -1.64 *
PINY 0.26 *
INF -0.05 **
CAD -0.83 *
[FD.sup.2] -
FD * DUM -0.51 ***
R2 = 0.99
Adjusted [R.sup.2] = 0.99
F-statistics = 1298.2
Dh Stat = 2.14
Dependent Variable ARDL Technique
Real GDP Growth Rate (Y) Order (2, 2, 2, 2, 0)
Re gessors
Coefficients
LFD 1.04 *
PINY 0.19 *
INF -0.06 ***
CAD -0.91
[FD.sup.2] -0.04 *
FD * DUM -
[R.sup.2] = 0.99
Adjusted [R.sup.2] = 0.99
F-statistics = 1576.2
Dh Stat = 1.81
Note: *, ** and *** represent Significant at 1 percent,
5 percent and 10 percent level of significance.
Table 6
Estimated Short Run Coefficients Using the ECM
Dependent Variable ARDL Technique
Change in Real GDP Growth Order (2, 2, 2, 2, 0)
Rate ([DELTA]Y) Regressors Coefficients
[DELTA]FD -0.28 **
[DELTA]PINV 0.17 ***
[DELTA]INF 0.08
[DELTA]CAD 0.98
[DELTA]FD*DUM -0.56
[EC.sub.t-1] -0.43
[R.sup.2] = 0.81
[R.sup.2] adjusted = 0.79
Note: *, ** and *** represent Significant at 1 percent,
5 percent and 10 percent level of significance.