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- Phillips–Perron_test abstract "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 , 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.".
- Phillips–Perron_test wikiPageID "24590400".
- Phillips–Perron_test wikiPageRevisionID "599105903".
- Phillips–Perron_test subject Category:Statistical_tests.
- Phillips–Perron_test comment "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 , where is the first difference operator.".
- Phillips–Perron_test label "Phillips–Perron test".
- Phillips–Perron_test label "Test de Phillips-Perron".
- Phillips–Perron_test sameAs Phillips%E2%80%93Perron_test.
- Phillips–Perron_test sameAs Test_de_Phillips-Perron.
- Phillips–Perron_test sameAs Q7185973.
- Phillips–Perron_test sameAs Q7185973.
- Phillips–Perron_test wasDerivedFrom Phillips–Perron_test?oldid=599105903.