ANALYSIS be non-stationary and a series when no unit

ANALYSISTECHNIQUE:Before analyzing the time-series data collectedthrough different sources for estimation of Time- series Model in StatisticalPackage 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 standardregression analysis that all the variables being tested should be stationary atlevel or at first difference.In statistics, a unit root test is a test to check stationarityof time series variables for using an autoregressive model because this problemis very common in time series data.

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A well-known test that is valid in largesamples is the augmented Dickey–Fuller test. These tests use the existence of aunit root as the null hypothesis. That is, the series with unit root present init, is said to be non-stationary and a series when no unit root is present issaid to be a stationary series.

There are many methods to test unit roots:·        Dickey Fuller·        Augmented Dickey Fuller·        Philips-Perron (PP) TestIn this study ADF is used as, this is the mostcommonly used unit root test by econometricians.4.10.CO-INTEGRATION TestThe two stage approaches are used to test theCo-integration of the variables and confirm whether there exists a long-termbalanced association or not (Engle and Granger, 1987).

The concept ofco-integration implies that even if many economic variables are non-stationary,their linear combination may be stationary through time (Greene, 2006). Spuriousresults will be obtained in case of having no stationary variable and having noco integration between the variables (Chan and Lee, 1997).For checking the co-integration this study used ARDLtechnique because some variables are stationary at level and some at firstdifference.THEERROR CORRECTION MODEL (ECM)After Finding thatvariables have the long-run co-integration, the short-term relationship amongvariables are find by applying the ECM. This approach is useful in finding bothshort-term and long-term response of time-series on other time-series.it findsthe speed with which the data of dependent variable restore to equilibrium bychange in other time series.The ECM is made by combining the error term with thefirst difference of the variables (short-run indicators).

This shows that thevariables have long run relationships.