In
statistics
Statistics (from German language, German: ', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a s ...
, the Phillips–Perron test (named after
Peter C. B. Phillips and
Pierre Perron) is a
unit root test. That is, it is used in
time series
In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. ...
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.
Davidson and MacKinnon (2004) report that the Phillips–Perron test performs worse in finite samples than the augmented Dickey–Fuller test.
References
{{DEFAULTSORT:Phillips-Perron test
Statistical tests