What is KPSS test used for?
In econometrics, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests are used for testing a null hypothesis that an observable time series is stationary around a deterministic trend (i.e. trend-stationary) against the alternative of a unit root.
What is the difference between ADF and PP test?
Though the PP unit root test is similar to the ADF test, the primary difference is in how the tests each manage serial correlation. Where the PP test ignores any serial correlation, the ADF uses a parametric autoregression to approximate the structure of errors.
Why is the ADF test preferred to the DF test?
The ADF test can handle more complex models than the Dickey-Fuller test, and it is also more powerful. That said, it should be used with caution because—like most unit root tests—it has a relatively high Type I error rate.
What is KPSS test in time series?
Introduction. The KPSS test, short for, Kwiatkowski-Phillips-Schmidt-Shin (KPSS), is a type of Unit root test that tests for the stationarity of a given series around a deterministic trend. In other words, the test is somewhat similar in spirit with the ADF test.
How does ADF test work?
In statistics and econometrics, an augmented Dickey–Fuller test (ADF) tests the null hypothesis that a unit root is present in a time series sample. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of confidence.
How do you interpret the results of ADF?
Augmented Dickey-Fuller test
- p-value > 0.05: Fail to reject the null hypothesis (H0), the data has a unit root and is non-stationary.
- p-value <= 0.05: Reject the null hypothesis (H0), the data does not have a unit root and is stationary.
What is cointegration test?
Cointegration tests analyze non-stationary time series— processes that have variances and means that vary over time. In other words, the method allows you to estimate the long-run parameters or equilibrium in systems with unit root variables (Rao, 2007).
Why is the Dicky Fuller test used?
Augmented Dickey Fuller test (ADF Test) is a common statistical test used to test whether a given Time series is stationary or not. It is one of the most commonly used statistical test when it comes to analyzing the stationary of a series.
What does Augmented Dickey Fuller test do?
In statistics and econometrics, an augmented Dickey–Fuller test (ADF) tests the null hypothesis that a unit root is present in a time series sample. The alternative hypothesis is different depending on which version of the test is used, but is usually stationarity or trend-stationarity.
What is Zivot Andrews test?
In the Zivot-Andrews tests, the null hypothesis is that the series has a unit root with structural break(s) against the alternative hypothesis that they are stationary with break(s). REject Null if t-value statistic is lower than tabulated critical value (left tailed test).
What is K in ADF test?
The k parameter is a set of lags added to address serial correlation. The A in ADF means that the test is augmented by the addition of lags. The selection of the number of lags in ADF can be done a variety of ways.
What is the cointegration test?