SSE/EFI Working Paper Series in Economics and Finance
Chien-Fu Lin and Timo Teräsvirta
Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters
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; (follow links to similar papers)
JEL-Codes: C22; (follow links to similar papers)
33 pages, May 1995
- This paper is published as:
Lin, Chien-Fu 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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