Testing the null hypothesis of stationarity against the alternative of a unit root¶
Why this mattered¶
TBD
Abstract¶
(no abstract available)
Related¶
- cite → Distribution of the Estimators for Autoregressive Time Series with a Unit Root — KPSS contrasts its stationarity-null test with Dickey and Fuller's unit-root asymptotics for autoregressive time-series estimators.
- cite → Testing for a unit root in time series regression — KPSS positions its stationarity-null test as the reverse-null complement to Phillips and Perron's unit-root regression test.
- cite → A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix — KPSS uses Newey and West's positive semi-definite HAC covariance estimator to handle serial correlation in the stationarity test statistic.
- enables ← Distribution of the Estimators for Autoregressive Time Series with a Unit Root — Dickey and Fuller's unit-root asymptotics supplied the nonstationary time-series testing framework that KPSS inverted by making stationarity the null hypothesis.
- enables ← A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix — Newey and West's HAC covariance estimator enabled KPSS to construct a robust Lagrange-multiplier stationarity test under serial correlation and heteroskedasticity.