Testing for coefficient stability of AR(1) model when the null is an integrated or a stationary process

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4 Citations (Scopus)

Abstract

In this paper, we propose a new test for coefficient stability of an AR(1) model against the random coefficient autoregressive model of order 1 neither assuming a stationary nor a non-stationary process under the null hypothesis of a constant coefficient. The proposed test is obtained as a modification of the locally best invariant (LBI) test by Lee [(1998). Coefficient constancy test in a random coefficient autoregressive model. J. Statist. Plann. Inference 74, 93-101]. We examine finite sample properties of the proposed test by Monte Carlo experiments comparing with other existing tests, in particular, the LBI test by McCabe and Tremayne [(1995). Testing a time series for difference stationary. Ann. Statist. 23 (3), 1015-1028], which is for the null of a unit root process against the alternative of a stochastic unit root process.

Original languageEnglish
Pages (from-to)2731-2745
Number of pages15
JournalJournal of Statistical Planning and Inference
Volume139
Issue number8
DOIs
Publication statusPublished - 2009 Aug 1

Keywords

  • Constancy
  • Random coefficient autoregressive model
  • Stability

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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