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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS
JANURAY 2011
VOL 2, NO 9
consideration that higher rates of capital formation, human capital, labor force growth and
increased capital goods imports may all be consequences of economic growth.
3.2 Data Sources
The data for the study are time series data covering the time period 1972-2005. They were
gathered from the Pakistan Economic Survey (various issues). The variables Kt, Xt, CMt, MXt,
SMXt and PXt represent gross fixed capital formation (proxy variable for capital), real total
exports, real imports of capital goods, real exports of manufactured goods, real exports of
semi manufactured goods and real exports of primary goods respectively. The non-export
output, NYt, is measured by real GDP net of total real exports. Non-export GDP, real total
exports, gross fixed capital formation, capital goods imports, exports of manufactured
products, semi manufactured exports and primary exports are evaluated in Pakistani rupees at
constant year 2000 prices. The labor force (Lt) represents the total number of employed people
each year.
This study has taken weighted average of enrolments at different schooling levels from
unpublished MPhil (Applied Economics) thesis by Iqbal (2007) to use it as a proxy variable
for human capital stock (Ht). Iqbal (2007) adopted slightly different method from Wang and
Yao (2003) in the construction of human capital index for Pakistan. He has taken the number
of enrolled students at different levels of schooling years instead of completed education due
to some constraints in the availability of data.
4. Methodology
To find out the long and short run dynamics between exports components and economic
growth, this study employs time series econometrics, such as cointegration and Vector Error
Correction Mechanism (VECM). These techniques are widely used in a Vector
Autoregressive (VAR) framework.
4.1 Johanson Cointegration and Error Correction Mechanism (ECM)
Johansen (1988), and Johansen and Juselius (1990) have developed a maximum likelihood
testing procedure on the number of cointegrating vectors within the VAR framework, which
also include testing procedures for linear restrictions on the cointegrating parameters, for any
set of variables. In our modeling we follow the general-to-specific approach advocated in
Hendry (1995). In particular, we start with general unrestricted VAR model with k lags:
� � A Z � � A Z � ………..…….....…….(8)
Zt � t t�1
... k t�k
� 6t
Where Zt is (n x 1) vector, � denotes the constant term, At…At-k are (n x n) matrices of
parameters and �6t is usual error term and IID. We test for cointegration and subsequently
impose the implied reduced rank restrictions on the unrestricted VAR model. Then we test for
the long-run exogeneity of the system variables. We use the results of the weak exogeneity
tests in order to build a parsimonious Error Correction Model (ECM) for the output that passes
all diagnostic tests, displays constant coefficients and possesses remarkable forecasting
properties. According to Granger (1986), the ECMs produce better short-run forecasts and
provide the short-run dynamics necessary to obtain long-run equilibrium.
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