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Phillips–Perron test — latest news, discussion & facts — IJR

Phillips–Perron test

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Phillips–Perron test

statistical test

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About Phillips–Perron test

In statistics, the Phillips–Perron test is a unit root test. That is, it is used in time series analysis to test the null hypothesis that a time series is integrated of order 1. It builds on the Dickey–Fuller test of the null hypothesis in , where is the first difference operator. Like the augmented Dickey–Fuller test, the Phillips–Perron test addresses the issue that the process generating data for might have a higher order of autocorrelation than is admitted in the test equation—making endogenous and thus invalidating the Dickey–Fuller t-test. Whilst the augmented Dickey–Fuller test addresses this issue by introducing lags of as regressors in the test equation, the Phillips–Perron test makes a non-parametric correction to the t-test statistic. The test is robust with respect to unspecified autocorrelation and heteroscedasticity in the disturbance process of the test equation.

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