SSE/EFI Working Paper Series in Economics and Finance,
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

JEL-codes: C22

33 pages, May 1995

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Published as

Chien-Fu Lin and Timo Teräsvirta, (1999), 'Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters', Journal of Econometrics, vol 90, pages 193-213

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