Before analyzing the time-series data collected
through different sources for estimation of Time- series Model in Statistical
Package EVIEWS 9. The stationarity of data is checked by using ADF test or unit root test .
4.7.3. UNIT-ROOT TEST
It is one of the assumptions of the standard
regression analysis that all the variables being tested should be stationary at
level or at first difference.
In statistics, a unit root test is a test to check stationarity
of time series variables for using an autoregressive model because this problem
is very common in time series data. A well-known test that is valid in large
samples is the augmented Dickey–Fuller test. These tests use the existence of a
unit root as the null hypothesis. That is, the series with unit root present in
it, is said to be non-stationary and a series when no unit root is present is
said to be a stationary series. There are many methods to test unit roots:
Augmented Dickey Fuller
Philips-Perron (PP) Test
In this study ADF is used as, this is the most
commonly used unit root test by econometricians.
The two stage approaches are used to test the
Co-integration of the variables and confirm whether there exists a long-term
balanced association or not (Engle and Granger, 1987). The concept of
co-integration implies that even if many economic variables are non-stationary,
their linear combination may be stationary through time (Greene, 2006). Spurious
results will be obtained in case of having no stationary variable and having no
co integration between the variables (Chan and Lee, 1997).
For checking the co-integration this study used ARDL
technique because some variables are stationary at level and some at first
ERROR CORRECTION MODEL (ECM)
After Finding that
variables have the long-run co-integration, the short-term relationship among
variables are find by applying the ECM. This approach is useful in finding both
short-term and long-term response of time-series on other time-series.it finds
the speed with which the data of dependent variable restore to equilibrium by
change in other time series.
The ECM is made by combining the error term with the
first difference of the variables (short-run indicators). This shows that the
variables have long run relationships.