Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters
AbstractThis 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.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 54.
Length: 33 pages
Date of creation: May 1995
Date of revision:
Publication status: Published in Journal of Econometrics, 1999, pages 193-213.
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Lack of identification; Lagrange multiplier test; parameter stability; return to normalcy; time-varying parameters; vector autoregressive process;
Other versions of this item:
- Lin, Chien-Fu Jeff & Terasvirta, Timo, 1999. "Testing parameter constancy in linear models against stochastic stationary parameters," Journal of Econometrics, Elsevier, vol. 90(2), pages 193-213, June.
- T. Teräsvirta & C. Lin, 1995. "Testing Parameter Constancy In Linear Models Against Stochastic Stationary Parameters," SFB 373 Discussion Papers 1995,28, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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