Stationary Test (Unit Root Test)
Nelson and Plosser (1982) argue that almost all macroeconomic time series one typically uses have a unit root. The presence or absence of unit roots helps to identify some features of the underlying data generating process of a series. In the absence of unit root (stationary), the series fluctuates around a constant long-run mean and implies that the series has a finite variance which does not depend on time. On the other hand, non- stationary series have no tendency to return to the long-run deterministic path and the variance of the series is time dependent. Non-stationary series suffer permanent effects from random shocks and thus the series follows a random walk. If the series is non-stationary and the first …show more content…
This test can encounter serial correlation by adding lagged off the dependent variable and the optimal lag length of the trial based on the minimum information criterion. So, ADF test is the alternative unit root test to improve the inadequacy of DF test. Reject null hypothesis ( ) if absolute t-statistic is higher than absolute critical value. Otherwise, do not reject . ii. Phillips- Perron test (PP)
Phillips- Perron test (1988) developed a generalization of the ADF test procedure that allows for fairly mild assumptions concerning the distribution of errors. The Phillips- Perron statistics are just modifications of the ADF t statistics that take into account the less restrictive nature of mistakes process. Reject null hypothesis ( ) if absolute t-statistic is higher than absolute critical value. Otherwise, do not reject . iii. Kwiatkowski-Phillips-Schmidt-Shin test (KPSS)
This test developed by Kwiatkowski, Philips, Schmidt, Shin (1992). It is intended to complement unit root test. This test is used as a stationary test to confirm the stationary of a series, and it can provide a more robust result. Reject null hypothesis ( ) if absolute t-statistic is higher than absolute critical value. Otherwise, do not reject