Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters

Chien-Fu Lin and Timo Teräsvirta

No 54, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: This paper considers testing parameter constancy in linear models when the alternative is that a subset of the parameters follow a stationary vector autoregressive process of known finite order. This kind of a linear model is only identified under the alternative, which usually precludes finding a test statistic with an analytic nuyll distribution. In the present situation, however, it is still possible to derive a test statistic with an asymptotic chi-squared distribution under the null hypothesis and this is done in the paper. The small-sample properties of the test statistic are investigated by simulation and found satisfactory. The test retains its power when the alternative to parameter constancy is a random walk parameter process.

Keywords: Lack of identification; Lagrange multiplier test; parameter stability; return to normalcy; time-varying parameters; vector autoregressive process (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 33 pages
Date: 1995-05
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Published in Journal of Econometrics, 1999, pages 193-213.

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Related works:
Journal Article: Testing parameter constancy in linear models against stochastic stationary parameters (1999)
Working Paper: Testing Parameter Constancy In Linear Models Against Stochastic Stationary Parameters (1995)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0054

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