Optimal Invariant Tests for the Autocorrelation Coefficient in Linear Regressions with Stationary or Nonstationary AR(1) Errors

Jean-Marie Dufour and Maxwell L. King

No 267065, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Inference on the autocorrelation coefficient p of a linear regression model with first-order autoregressive normal disturbances is studied. Both stationary and nonstationary processes are considered. Locally best and point-optimal invariant tests for any given value of p are derived. Special cases of these tests include tests for independence and tests for unit root hypotheses. The powers of alternative tests are compared numerically for a number of selected testing problems and for a range of design matrices. The results suggest that point-optimal tests are usually preferable to locally best tests, especially for testing values of p greater than or equal to one.

Keywords: Research and Development/Tech Change/Emerging Technologies; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 47
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Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267065

DOI: 10.22004/ag.econ.267065

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