Abstract
In this paper we propose a new test for parameter constancy of linear regressions. The alternative hypothesis is that the data set can be split up in a finite number of subsets such that at least two of the OLS estimators corresponding to these subsets have different probability limits. Conditions are set forth such that under the null hypothesis the test statistic involved is asymptotically χ2 distributed, whereas under the alternative hypothesis the test statistic converges in probability to infinity. These conditions allow for stochastic regressors and time series applications.
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References
Bierens, H.J., 1982, “Consistent Model Specifications Tests”, Journal of Econometrics 20, 105–134.
Bierens, H.J., 1984, “Model Specification Testing of Time Series Regressions”, Journal of Econometrics 27 (forthcoming).
Brown, R.L., J. Durbin and J.M. Evans, 1975, “Techniques for Testing the Constancy of Regression Relationships over Time” (with discussion), Journal of the Royal Statistical Society, Series B 37, 149–192.
Quandt, R.E., 1960, “Tests of the Hypothesis that a Linear Regressions System Obeys Two Separate Regimes”, Journal of the American Statistical Association 55, 324–330.
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© 1984 Springer-Verlag Berlin Heidelberg
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Bierens, H.J. (1984). Testing Parameter Constancy of Linear Regressions. In: Dijkstra, T.K. (eds) Misspecification Analysis. Lecture Notes in Economics and Mathematical Systems, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-95461-0_7
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DOI: https://doi.org/10.1007/978-3-642-95461-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-13893-8
Online ISBN: 978-3-642-95461-0
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