1
Abstract
The present study is confined to examine the static and dynamic causality amongst the sectoral incomes of GDP, and GDPs of Agriculture, Industry, and Services during the period

2
1972 to 2011. Various econometric tests, such as Granger Causality, impulse response and variance decomposition were applied on the time series data. Pair wise Granger causality shows a unilateral directional granger causality between, sectors of GDP and services, industry and agriculture, industry and services and bidirectional causality between GDP and agriculture. Granger causality under VAR environment shows a bidirectional long run relationships between GDP- industry, GDP - services, and industry-service series. Dynamic causality results show that contribution to GDP forecast error by agriculture sector is highest followed by services and industry. Contribution to agriculture forecast error by GDP sector is highest followed by services and industry. Contribution to industry forecast error by GDP sector is highest followed by agriculture and services. Contribution to services forecast error by GDP sector is highest followed by agriculture and industry. This study gives guidelines to the economic policy makers.
Key Words: Granger Causality Test, GDP, Agriculture, Industry, Services, Pakistan
I. INTRODUCTION
Pakistan faced a number of problems at the time of independence in 1947, such as refugees from
India that created problems as the country had no resources to meet them. Migration of Hindu

3 and Sikh communities paralyzed the economic and administrative manpower Ashfaque H. Khan
(2012). Economy of Pakistan has suffered from internal political disputes, growing population, and low investment. Real GDP growth % varies from a lowest value of 1.7% in 2008-09 and a highest value of 9.0 % in 2004-05 with an average of about 4.7 % from the year 1950 to 2010-11
(Fig.1). One of the reason for this low economy is due to the constant pressure of the War on
Terror in Afghanistan and spread of terrorist activities into settled areas of Pakistan. This has resulted destruction of infrastructure, growing unemployment and economic activity to a standstill. The cost of war on terror for the period 2001-2011 amounts to about 5036.8 billion Rs.
Agriculture sector is playing a central role in the economy of Pakistan. It accounts for over 25 percent of GDP and is the largest employer, absorbing about 45 % of the total labour force.
About 62 % of the population is living in rural areas, and depends on agriculture for their livings.
The agriculture sector supplies raw materials to the downstream industries and at the same time is a large market for fertilizers, pesticides and agricultural machinery such as tractors and other implements used in agriculture. Agriculture sector has been on the decline (Table 1).
Table 1: Historical Growth Performance
Years
1960 , s
1970
, s
1980 , s
1990
, s
2000 , s
Average Growth
Percent
5.1
2.4
5.4
4.4
3.2
Source: Fedral Bureou of Statistics
According to Iqbal et al; (2003) ,Govt. of Pakistan (2011), agriculture growth rate of more than
5% is necessary to attain a rapid increase in national income. Amjad, (2009) described the need of technical and scientific development for a sustained increase in agriculture production.
At the time of independence Pakistan got only 34 industries out of 955. However with the passage of time Pakistan utilized its domestic resources as well as foreign assistances for development of industrial sector. Industrial development is of great importance for the economic development of a country. Countries with strong industrial base have showed more rapid economic growth. Industrial sector of Pakistan is contributing about 25 % of GDP. Services sector is the fastest growing sector in the economy of world. Kongsamut, et al.(2001) during their studies of 123 countries for the period 1970-80 observed that increase in services increases per capita GDP in the economy of these countries and also that transfer of economies from agriculture sector to services is higher as compared to transfer of economies from industry. Rath, et al.(2006) observed that higher growth in services sector in India leads to its economic growth.
In Pakistan, over a period of time shares of service sectors are increasing in all sectors of economy. Katircioglo (2008) observed that in contrast with the path of transition from agrarian to industrial economy being followed by some western economies, developing economies are following rapid industrialization.
This paper consists of six sections. Section I is Introduction of the study. Section II covers
Literature Review. Section III is.Data and Estination Methodology . Section IV is about Results and Discussion . Section V consists of Conclusion and VI is References.

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II . LITERATURE REVIEW
The service sector has provided steady growth in economy of Pakistan and at present its share is more than 50 % , and helps to reduce poverty alleviation and improve quality of life Ayaz,et al.
(2011). The authors also suggested to improve research and development, technology up gradation and human resource development Subramanium and Reed (2009) discussed the linkages between agriculture, manufacturing service and trade sector for Poland and Romania.
Katircioglo,Salih (2008) observed unidirectional causality from GDP to agriculture industry and services expansion. In addition agriculture production show unidirectional causality to industry and services show causality to both industry and agriculture sector. Ismail, et al. ( 2012 ) applied
VECM to study linkages , among agriculture, industry, construction , transport, storage and communication and service sectors and observed their interdependent growth rates. The authors also reported that industrial and construction sectors contribute positively to agriculture sector while service sector contribute negatively. Patrick Enu, et al. (2013) during their study of growth rate of Ghana concluded that a 1% increase in agriculture sector, service sector, and industry sector causes GDP to grow by 0.45 %, 0.38%, 0.18% in GDP growth respectively. Mikael
Linden and Tahir Mahmood (2007), during their study report of a long run relationship among agriculture, industry, service and to economic growth. Hina Safdar, et al. (2012) ,from their empirical study of co –integration observed that agricultural productivity and employment in agriculture sector in Pakistan are positively and significantly associated with economic growth.
III. DATA AND ESTIMATION METHODOLOGY
During the present study econometric model consists of GDP as dependent variable, and
GDPs of Agriculture(AGR), Industry(IND), and Services(SER) as independent variables.. Test equation method has been used to select the best regression method of the variables. The model
GDP as the dependent variable and GDPs of AGR,IND, and SER as independent variables came out to be the best regression model fulfilling the major requirements of a good regression model.
MODEL
GDP = α + β
1
(AGR) + β
2
(IND ) + β 3(SER) + Ut
Where
GDP = Gross domestic product
AGR=Agriculture
IND=Industry
SER =Services
Ut = Error term, covering the left over effects.
Annual data on GDP, GDPs of agriculture ,industry, and services sectors at constant prices
(2000) for 1972 to 2011 was collected from world bank national accounts data ,and IMF. The present study is confined to study the static and dynamic causal relationship among these variables. Various tests such as Augmented Dickey-Fuller for unit roots, Johansen test for cointegration, VECM for short run dynamics, CUSUM for stability test of the model, Granger

5 causality tests for causality relationship between the variables , Impulse response of the variables and Variance decomposition tests for dynamic causality were carried out. The model selected is not spurious as it has all the major requirements of a good regression model.
IV. RESULTS AND DISCUSSION
The main purpose of the regression analysis is to find out the static and dynamic causal relationship among GDP and sectoral GDPs of agriculture, industry, and service sector. The results of regression analysis are shown in Table (2). The regression accounts for 99.93 % of the variation in the dependent variable, with estimated standard deviation of the error term, 0.69 % and Durbin-Watson stat; 1.074581 (higher than R-squared). The F-statistics (F-statistic =
17196.65 with Prob .< .05) show the independent variables are significant to effect dependent variable. ( Moreover Jarque- Bera ,3.842050 (Prob. 0.146457), Heteroskedasticity Test: Breusch-
Pagan-Godfrey (Obs*R-squared- 2.178596, Prob. Chi-Square(3)- 0.5362) and CUSUM plot (Fig
1) show the plot lies between 5 % critical lines. All these observations support that the model selected is good and stable regression model.
Table 2: Regression Model Estimated Equation
Variable
C
AGR
IND
SER
R-squared
Adjusted R-squared
S.E. of regression
UNIT ROOT TESTS
Coefficient
1.979459
0.137122
0.736433
0.015120
0.999303
0.999245
0.006949
Std. Error
0.210638
0.045186
0.037274
0.022088 t-Statistic
9.397434
3.034624
19.75735
0.684506
F-statistic
Prob(F-statistic)
Durbin-Watson stat
Prob.
0.0000
0.0045
0.0000
0.4980
17196.65
0.000000
1.074581
Augmented Dicky Fuller (ADF) (1979) test was carried out for stationarity of endogeneous and exogeneous variables to avoid spurious model. Unit root test is done to check whether the selected variables are integrated of order I(1) before proceeding for Johansen Co-integration test.
The results are shown in Table (3).
Table 3: Results for Unit Root Test ( Augmented Dickey- Fuller Test)
Series
GDP
D(GDP)
AGR
D(AGR)
IND
D(IND)
SER
D(SER)
Test Statistics
-2.481777
-4.402548
-0.260091
-7.854249
-2.120547
-4.398749
-0.498271
-6.569093
Critical Value 5%
-2.938987
-2.941145
-2.938987
-2.941145
-2.938987
-2.941145
-2.938987
-2.941145
Result
Non Stationary
Stationary
Non Stationary
Stationary
Non Stationary
Stationary
Non Stationary
Stationary
The table shows that the variables are non stationary at level and becomes stationary at first difference. Since all the variables are integrated of the order I(1) , we can proceed for co-

integration analysis. To proceed for co-integration, the first step is to find out optimal lag length. VAR Lag Order Selection Criteria based on AIC has been used for optimal lag length, which gave one optimal lag length to be used for Johanson co-integration tests as shown in
Table (4). So we have used lag interval (1,1)for the tests. The co-integration test is done to find out whether a group of non-stationary series is co-integrated or not. During the present study
Johansen (1988) co-integration maximum likelihood method of co- integration was used to determine number of co-integrating vectors and the results are shown in Tables (5,6) .
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Table: 4: VAR Lag Order Selection Criteria
Endogenous variables: GDP AGR IND SER
Lag LogL LR FPE AIC SC HQ
4
5
6
0
1
2
3
269.6976 NA 1.92e-12 -15.62927 -15.44970 -15.56803
409.7436 238.9020* 1.31e-15* -22.92609 -22.02823* -22.61990
419.4323
435.7066
14.24814
20.10354
1.98e-15
2.18e-15
-22.55484
-22.57098
-20.93869
-20.23654
-22.00369
-21.77487
458.3467
474.2298
510.8441
22.64010
12.14593
19.38402
1.86e-15
2.89e-15
1.93e-15
-22.96157
-22.95470
-24.16730*
-19.90885
-19.18369
-19.67800
-21.92051
-21.66867
-22.63632*
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
Table 5: (Co-integration tests based on Johansen’s Maximum Likelihood Method)
Hypothesized
No. of CE(s)
None *
At most 1
At most 2
At most 3 *
Eigen value Trace
GDP = F (AGR,IND,SER)
0.05Critical Hypothesized statistics Value No. of CE(s)
0.488348 52.08639 47.85613
0.302590 26.62216 29.79707
0.183400 12.92766 15.49471
0.128549 5.228645 3.841466
None
At most 1
At most 2
Max-Eigen
Statistics
25.46423
13.69450
0.05 Critical
Value
27.58434
21.13162
7.699012 14.26460
At most 3 * 5.228645 3.841466
Trace and Max-Eigen tests indicates 1 co-integrating eqn at the 0.05 and 0.10 levels respectively.
* denotes rejection of the hypothesis at 0.05 and 0.10 levels for Trace and Max-Eigen Statistics

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Table 6: Result for the Estimated Long Run Coefficient
Dependent Variable GDP
Regressor
AGR
IND
SER
Period(1972-2011)
Coefficient
0.072570
(0.06974)
-1.015519
(0.06182)
0.121318
(0.03619)
The normalized co -integrating coefficients on GDP are given in Table 5 which show elasticities of AGR, IND, and SER as 0.07, - 1.02, and .12 respectively.
SHORT RUN DYNAMICS
The results of error correction model estimates are given in Table (7). The coefficient of speed of adjustment is negative and indicates that GDP, AGR, IND, and SER are co-integrated. The results also show that 18 % of disequilibrium is corrected each period towards the long run periods. The immediate impact of the explanatory variables shows that during the past one year
Agr; and IND,SER had negative and positive impacts on GDP though not significant with elasticities of 0.129,0.086, and0.038 respectively.
Table 7: ECM Estimates ( Dependent variable GDP)
EC( -1) D(AGR(-1)) D(IND(-1)) D(SER(-1)) Constant R-squared
-0.182233
(0.16147)
[-1.128 61]
-0.128744
(0.10526)
[-1.22314]
0.085641
(0.15684)
[ 0.54605]
0.038245
(0.03314)
[ 1.15410]
0.015300
(0.00373)
[ 4.09788]
0.220221
GRANGER CAUALITY TESTS.
Granger (1996) causality tests have been carried out to study the linear causation among the variables. Using the optimal lag length (1), the results obtained are shown in Tables (8,9).
1.Pair wise Granger Causality Tests
Table 8: Pair wise Granger Causality Test
Null Hypothesis:
AGR does not Granger Cause GDP
GDP does not Granger Cause AGR
IND does not Granger Cause GDP
GDP does not Granger Cause IND
SER does not Granger Cause GDP
F-Statistic Prob.
4.63099 0.0382
9.49223 0.0039
0.88081 0.3542
3.33144 0.0763
0.14751 0.7032

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GDP does not Granger Cause SER
IND does not Granger Cause AGR
AGR does not Granger Cause IND
SER does not Granger Cause AGR
AGR does not Granger Cause SER
SER does not Granger Cause IND
IND does not Granger Cause SER
10.1265 0.0030
10.3577 0.0027
0.29626 0.5896
2.69427 0.1094
2.52225 0.1210
3.60613 0.0656
10.3774 0.0027
Table (8): shows a unilateral directional causality between GDP-SER, IND-AGR, IND-SER and bidirectional causality between GDP and AGR.
2, Granger Causality under VAR Environment
Table 9: Long run and Short run Granger Causality
GDP
-
AGR
AGR-
GDP
GDP-
IND
IND-
GDP
GDP-
SER
SER-
GDP
AGR-
IND
IND-
AGR
AGR-
SER
SER-
AGR
IND-
SER
SER-
IND
LR None None Yes Yes Yes Yes None None None None Yes Yes
SR None None None None None None None None None None None None
Granger causality analysis given in Table (9) shows bidirectional long run relationships between GDP-IND , GDP-SER, and IND-SER series at 5 % level,
IMPULSE RESPONSE OF THE VARIABLES
If one standard deviation positive shock is given to GDP, AGR, IND, and SER the impulse response of the variables over a period of ten years is shown in Fig. (2). The figure shows that innovations in other sectors have a positive effect on GDP over a period of ten years. The innovations in service have a positive effect on industry during first four years and then becomes negative. Innovations in agriculture have a negative effect on GDP, industries, and services.
VARIANCE DECOMPOSITION
Variance decomposition provides a way of finding out the relative importance of shocks to each determinant of GDP in explaining variations in the variance decomposition analysis that show the proportion of the forecast error .Variance decomposition in GDP explained by its own innovations and innovations in its determinants are given Table 9. This study describes the variance decomposition in GDP and also analyze the relative importance of each determinants effecting its movements. This study consists of the variance decompositions for 10 years to observe the effects when the independent variables are allowed to affect the GDP. For the 3 rd year ahead forecast error is the short term effect and after a period of 10 years is the long term and shown in Table 9. As described in Brooks (2002) all the variance in GDP in first year is explained by its own shocks. The Table (10) shows variance decomposition of the variables for the short run shock, long run shock along with component variation.

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Table 10: Variance Decomposition
Variables Short run Shock%
Variance Decomposition of GDP
GDP
AGR
IND
92.59099
6.321054
0.957846
SER 0.130114
Variance Decomposition of AGR
GDP 33.01502
AGR
IND
SER
64.88237
0.956578
1.146037
Variance Decomposition of IND
GDP 80.57221
AGR 6.234928
IND
SER
10.68514
2.507725
Variance Decomposition of SER
GDP 24.86108
AGR
IND
SER
5.162022
1.786438
68.19046
Long run Shock%
77.60626
19.90821
0.370138
2.115394
61.60081
36.77707
0.583095
1.039030
73.85168
19.36365
3.857077
2.927595
36.75878
18.51336
1.140425
43.58744
Component Variation
14.98473
-13.5872
0.587708
-1.98528
-28.5858
28.1053
0.373483
0.107007
6.72053
-13.1287
6.828063
-0.41987
-11.8977
-13.3513
0.646013
24.60302
One standard deviation innovation in AGR,IND,SER explain 19.9%,,0.37%, and 2.1% of forecast error variance of GDP respectively.
One standard deviation innovation in GDP,IND,SER explain 61.6%,,0.58%, and 1.04% of forecast error variance of AGR respectively
.One standard deviation innovation in GDP AGR,,SER explain73.85 19.36%,, and 2.92% of forecast error variance of IND respectively.
One standard deviation innovation in GDP, ,IND, AGR explain36.76%,1.14% and 18.51% of forecast error variance of SER respectively.
V. CONCLUSION
During the present study results of causality and co-integration relationship between GDPs of agriculture, industry, service and total GDP of Pakistan is described. As the selected variables show first order integration I(1) ,Johansen co-integration test, VECM, Impulse response and
Variance decomposition analysis to examine the static and dynamic relationship among the variables. Grenger causality analysis given in Table 8 shows bidirectional long run relationships between GDP-Industry , GDP-Service, and Industry-Service series at 5 % level. Pair wise
Granger Causality Tests show unilateral directional causality between GDP-Service, Industry-

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Agriculture, Industry-Services and bidirectional causality between GDP and Agriculture. The results of Impulse Response of the Variables show that innovations in other sectors have a positive effect on GDP over a period of ten years. The innovations in service have a positive effect on industry during first four years and then becomes negative. Innovations in agriculture have a negative effect on GDP, industries, and services. The results of variance decomposition show that contribution in GDP, forecast error of agriculture sector is highest followed by services and industry sectors. The contribution in agriculture sector, forecast error of GDP is highest followed by services and industry sectors. The contribution in industry sector, forecast error of GDP is highest followed by agriculture and services sectors. The contribution in service sector, forecast error of GDP is highest followed by agriculture and industry sectors. From the above mentioned study we can conclude that all the variables are interdependent and need due attention by the economic policy makers.
VI. REFERENCES
Amjad R(2009). Key challenges facing Pakistan agriculture: How best can policy makers respond? A note. Paper presented in the GDN 11 th annual conference held on 16-18,(January
2010) at Prague Czech Republic.
Ashfaque H. Khan (2012) Pakistan’s Economy: Challenges and prospects .
Presentation At the
Institute of Chartered Accountants of Pakistan and CFO Conference, Karachi.)
Ayaz Ahmad and Henna Ahsan (2011) Contribution of services sector in the economy of
Pakistan.
PIDE working paper 2011:79
Brooks C (2002.) Introductory Econometrics for Finance Cambridge University
Dickey,D.A. and W.A. Fuller (1979).Distribution of the estimates for autoregressive time series with a unit root. Journal of the American Statistical Association, vol.74, pp.427-431.
GOP (2011).Pakistan Economic Survey (2010-2011), published by the Ministry of Finance..
Government of Pakistan, Islamabad .
Granger, C.W.j. (1996) Some recent developments in a concept of causality. Journal of
Econometrics 39: pp.199-211.
Hina Safda, Sahreen Maqsood , Sami ullah (2012) Impact of agriculture volatility on economic growth: A case study of Pakistan.
J.Asian,Dev.Studies
,vol 1,issue 2.pp.24-34
Iqbal M, Munir A,Abbas K (2003). The impact of institutional credit on agricultural production in Pakistan . The Pak.Dev.Rev
;42(4) 469-485
Ismail, M.H; Esmat Ara, Papadopoulou,E (2012). Sectoral co-integration and the role of agriculture in Bangladesh . Pakistan Journal of Applied Economics , vol.22 Nos.1 &2,pp.39-52)
Johansen, S. (1988), Statistical analysisof co integration vectors. Journal of Economic Dynamics and Control, Vol.12,pp.231-254.

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15
10
5
0
-5
-10
-15
-20
11
Katircioglo,Salih (2008). Cointe gration and causality between GDP, Agriculture,Industry and
Services growth in north Cyprus:Evidence from time series data 1977-2002.
Review of Social
Economics and Business studies , 5/6 173-187.
Kongsamut,P;S.Rebelo,and Danyang Xie (2001) Beyond balanced growth.
IMF working paper ,WPl01l85,june.
Mikael Linden and Tahir Mahmood (2007).Long run relationships between sector shares and economic growth –A Panel data analysis of the Schengen region.
Keskustelualoitteita No.50
Joensunn yliopisto,Taloustieteet .pp.1-36
Patrick Enu, E.D.K.Havi, F.Osei-Gyimah, Prudence Attah-Obeng, C.D.K.Opoku (2013)
Achieving higher GDP growth rates in Ghana: Which sector is to lead.Part II Social Sciences and
Humanities .Vol.4,No.4 pp.457-468
Rath,D.P. and R.Rajesh (2006) Analytic and implications of services sector growth in Indian economy.
The journal of income and wealth 28:1
Subramanium, V; and Reed,M (2009) Agricultural inter-sectoral linkages and its contribution to economic growth in the transition countries. Contributed paper prepared for presentation at the
International associationof agricultural economists conference, Beijing. China.
1980 1985 1990
CUSUM
1995 2000
5% Signific anc e
2005 2010
Fig. 1: CUSUM plot with 5 % Critical Lines

12
Res pons e of GDP to Choles ky
One S.D. Innovations
Res pons e of AGR to Choles ky
One S.D. Innovations
.012
.008
.004
.000
-.004
.016
.012
.008
.004
.000
-.008
1
.016
.012
.008
.004
.000
-.004
-.008
1
2 3
GDP
4 5
AGR
6 7
IND
8 9
SER
10
-.004
1 2 3
GDP
4 5
AGR
6 7
IND
8 9
SER
10
Res pons e of IND to Choles ky
One S.D. Innovations
2 3
GDP
4
Res pons e of SER to Choles ky
One S.D. Innovations
5
AGR
6
.02
.01
.00
-.01
-.02
.05
.04
.03
7
IND
8 9
SER
10 1 2 3
GDP
4 5
AGR
6 7
IND
8 9
SER
10
Fig.2 : Impulse Response of Variables