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Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters

Author

Listed:
  • Lin, Chien-Fu
  • Teräsvirta, Timo

    (Dept. of Economic Statistics, Stockholm School of Economics)

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.

Suggested Citation

  • Lin, Chien-Fu & Teräsvirta, Timo, 1995. "Testing Parameter Constancy in Linear Models against Stochastic Stationary Parameters," SSE/EFI Working Paper Series in Economics and Finance 54, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0054
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    Cited by:

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    2. 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.
    3. Karmakar, Sayar & Richter, Stefan & Wu, Wei Biao, 2022. "Simultaneous inference for time-varying models," Journal of Econometrics, Elsevier, vol. 227(2), pages 408-428.
    4. Mikihito Nishi, 2023. "Testing for Stationary or Persistent Coefficient Randomness in Predictive Regressions," Papers 2309.04926, arXiv.org, revised May 2024.
    5. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    6. 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.

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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