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 Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 1995,28.
Date of creation: 1995
Date of revision:
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.
- Lin, Chien-Fu & Teräsvirta, Timo, 1995. "Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters," Working Paper Series in Economics and Finance 54, Stockholm School of Economics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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