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

研究成果: Article

4 引用 (Scopus)

抄録

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.

元の言語English
ページ(範囲)2731-2745
ページ数15
ジャーナルJournal of Statistical Planning and Inference
139
発行部数8
DOI
出版物ステータスPublished - 2009 8 1
外部発表Yes

Fingerprint

Stationary Process
Null
Locally Best Invariant Test
Testing
Random Coefficient Models
Coefficient
Unit Root
Autoregressive Model
Time series
Nonstationary Processes
Model
Monte Carlo Experiment
Null hypothesis
Coefficients
Stationary process
Integrated
Experiments
Alternatives

ASJC Scopus subject areas

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

これを引用

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