ANALYSIS

TECHNIQUE:

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:

·

Dickey Fuller

·

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.

4.10.

CO-INTEGRATION Test

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

difference.

THE

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.