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 InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 90 (1999)
Issue (Month): 2 (June)
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Web page: http://www.elsevier.com/locate/jeconom
Other versions of this item:
- 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.
- 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
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- Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June.
- Pagan, Adrian, 1980. "Some identification and estimation results for regression models with stochastically varying coefficients," Journal of Econometrics, Elsevier, vol. 13(3), pages 341-363, August.
- Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
- Min, Chung-ki & Zellner, Arnold, 1993.
"Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates,"
Journal of Econometrics,
Elsevier, vol. 56(1-2), pages 89-118, March.
- Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers 90-92-23, California Irvine - School of Social Sciences.
- Shively, Thomas S., 1988. "An analysis of tests for regression coefficient stability," Journal of Econometrics, Elsevier, vol. 39(3), pages 367-386, November.
- Watson, Mark W & Engle, Robert F, 1985. "Testing for Regression Coefficient Stability with a Stationary AR(1) Alternative," The Review of Economics and Statistics, MIT Press, vol. 67(2), pages 341-46, May.
- Russell Davidson & James G. MacKinnon, 1985.
"Heteroskedasticity-Robust Tests in Regression Directions,"
616, Queen's University, Department of Economics.
- DAVIDSON, Russel & MACKINNON, James G., . "Heteroskedastcity-robust tests in regressions directions," CORE Discussion Papers RP -678, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Dufour, Jean-Marie, 1982. "Recursive stability analysis of linear regression relationships: An exploratory methodology," Journal of Econometrics, Elsevier, vol. 19(1), pages 31-76, May.
- Donald W.K. Andrews, 1990.
"Tests for Parameter Instability and Structural Change with Unknown Change Point,"
Cowles Foundation Discussion Papers
943, Cowles Foundation for Research in Economics, Yale University.
- Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
- Bos, T & Newbold, P, 1984. "An Empirical Investigation of the Possibility of Stochastic Systematic Risk in the Market Model," The Journal of Business, University of Chicago Press, vol. 57(1), pages 35-41, January.
- Brooks, Robert D., 1993. "Alternative point-optimal tests for regression coefficient stability," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 365-376.
- Farley, John U. & Hinich, Melvin & McGuire, Timothy W., 1975. "Some comparisons of tests for a shift in the slopes of a multivariate linear time series model," Journal of Econometrics, Elsevier, vol. 3(3), pages 297-318, August.
- Gebrenegus Ghilagaber, 2004. "Another Look at Chow's Test for the Equality of Two Heteroscedastic Regression Models," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(1), pages 81-93, February.
- Terasvirta, Timo, 2006.
"Forecasting economic variables with nonlinear models,"
Handbook of Economic Forecasting,
- Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
- Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
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