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

Research output: Contribution to journalArticle

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
Externally publishedYes

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

Keywords

  • Constancy
  • Random coefficient autoregressive model
  • Stability

ASJC Scopus subject areas

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

Cite this

@article{3f871362b62442a7a2683821f2e93482,
title = "Testing for coefficient stability of AR(1) model when the null is an integrated or a stationary process",
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.",
keywords = "Constancy, Random coefficient autoregressive model, Stability",
author = "Daisuke Nagakura",
year = "2009",
month = "8",
day = "1",
doi = "10.1016/j.jspi.2008.12.009",
language = "English",
volume = "139",
pages = "2731--2745",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",
number = "8",

}

TY - JOUR

T1 - Testing for coefficient stability of AR(1) model when the null is an integrated or a stationary process

AU - Nagakura, Daisuke

PY - 2009/8/1

Y1 - 2009/8/1

N2 - 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.

AB - 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.

KW - Constancy

KW - Random coefficient autoregressive model

KW - Stability

UR - http://www.scopus.com/inward/record.url?scp=67349139603&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67349139603&partnerID=8YFLogxK

U2 - 10.1016/j.jspi.2008.12.009

DO - 10.1016/j.jspi.2008.12.009

M3 - Article

VL - 139

SP - 2731

EP - 2745

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 8

ER -