ADF (Augmented Dickey-Fuller) and KPSS (Kwiatkowski-Phillips-Schmidt-Shin) are two common statistical tests for stationarity. The purpose of these two tests is technically the same, except for the technique used behind.
1. There are six different unit root test available in Eviews: The Augmented Dickey-Fuller (ADF) Test. Dickey-Fuller Test with GLS Detrending (DFGLS) The Phillips-Perron (PP) Test. The Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) Test. Elliot, Rothenberg, and Stock Point Optimal (ERS) Test. Ng and Perron (NP) Tests.
Conversely, the KPSS test is a stationarity test in which its null hypothesis is stationarity while its alternative hypothesis is non-stationarity. Augmented Dickey-Fuller Test. The command to do the ADF test is adf.test() in R. We will use this test on the series using both the alternative hypothesis of stationarity.
In the second part of the series, we will be testing for non-stationarity using the Augmented Dickey-Fuller, the Phillips Perron Test, and the KPSS test. Cod
l is the KPSS statistic for the null hypothesis of trend stationarity, where s2 l is a consistent estimate of the long-run variance of the series (usually computed as the Newey-West robust estimate of the variance, using llags of the series). The KPSS test has also been employed as a test for fractional integra-tion.
. 773 391 669 240 459 159 327 513
kpss test vs adf test
