The most commonly used is the ADF test, where the null hypothesis is the time series possesses a unit root and is non-stationary. So, id the P-Value in ADH test is less than the significance level (0.05), you reject the null hypothesis. The KPSS test, on the other hand, is used to test for trend stationarity. The null hypothesis and the P-Value kpss vs adf test with example and python code (Time Series Forecasting) The Augmented Dickey Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test are two Ignoring this complication, though, the process for performing an ADF test in Stata is no different from performing the standard DF test. In fact, the command is the same. You must simply add a certain number of lagged differences via lags( k) as an option to dfuller. For example, an ADF for an AR(2) vs a random walk is dfuller X, nocons lags( 1). kpss.test is the one you are looking for, considering you only look for stationary series, if you use adf.test or pp.test you must know that this functions test is for "trend-Stationary", so its is not what you are looking for. i only use Box.test for residuals auto correlation test for Arima models for example. As far i know this test only take care of mean, not variance or covariance. Stochastic trends can be detected using unit root tests. For example, the augmented Dickey-Fuller test, or the KPSS test. Augmented Dickey-Fuller (ADF) test. The ADF test checks whether an auto-regressive model contains a unit root. The hypotheses of the test are: Null hypothesis: There is a unit root (the time series is not stationary); Details. This function combines the existing functions adf.test, pp.test and kpss.test for testing the stationarity of a univariate time series x.. Value. The results are the same as one of the adf.test, pp.test, kpss.test, depending on which test are used.. Note. Missing values are removed. Author(s) Debin Qiu .

kpss test vs adf test