Phillips–Perron test
Encyclopedia
In statistics
, 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
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 Δ 
, where Δ 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 Δ 
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
.
Statistics
Statistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....
, the Phillips–Perron test (named after Peter C. B. Phillips and Pierre Perron) is a unit root
Unit root
In time series models in econometrics , a unit root is a feature of processes that evolve through time that can cause problems in statistical inference if it is not adequately dealt with....
test. That is, it is used in time series
Time series
In statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones index or the annual flow volume of the...
analysis to test the null hypothesis
Null hypothesis
The practice of science involves formulating and testing hypotheses, assertions that are capable of being proven false using a test of observed data. The null hypothesis typically corresponds to a general or default position...
that a time series is integrated of order
Order of integration
Order of integration, denoted I, is a summary statistic for a time series. It reports the minimum number of differences required to obtain a stationary series.- Integration of order zero :...
1. It builds on the Dickey–Fuller test of the null hypothesis
in Δ , where Δ 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 Δ as regressors in the test equation, the Phillips–Perron test makes a non-parametricNon-parametric statistics
In statistics, the term non-parametric statistics has at least two different meanings:The first meaning of non-parametric covers techniques that do not rely on data belonging to any particular distribution. These include, among others:...
correction to the t-test statistic. The test is robust with respect to unspecified autocorrelation
Autocorrelation
Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time separation between them...
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
Augmented Dickey-Fuller test
In statistics and econometrics, an augmented Dickey–Fuller test is a test for a unit root in a time series sample. It is an augmented version of the Dickey–Fuller test for a larger and more complicated set of time series models....
.
Reference
- Davidson, Russell and James G. MacKinnon (2004), Econometric Theory and Methods, p.623, ISBN 978-0195123722
- Phillips, P.C.B and P. Perron (1988), "Testing for a Unit Root in Time Series Regression", BiometrikaBiometrika- External links :* . The Internet Archive. 2011....
, 75, 335–346