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The Chow Test with Time Series-Cross Section Data

Author

Listed:
  • James K. Binkley
  • Jeffrey S. Young

Abstract

The Chow test is the standard method to test for differences in regression response across groups. In some cases, the groups being tested are composed of a time series of cross sections. For example, when testing for differences across industries, each industry may be composed of several observations on several individual firms. If the individuals themselves have systematic differences, the Chow test will be compromised: the individual and group effects become confounded. This can cause rejections in the absence of the group effect of interest. We illustrate the problem with a Monte Carlo analysis, and show that the effects cannot be separated. We propose a bootstrap-like testing procedure that can eliminate excessive Type I errors, and when used with the standard Chow test can help to arrive at an appropriate conclusion when both effects are present. JEL classification numbers: C01, C10, C12, C15, C18.

Suggested Citation

  • James K. Binkley & Jeffrey S. Young, 2023. "The Chow Test with Time Series-Cross Section Data," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 12(1), pages 1-3.
    • Handle: RePEc:spt:stecon:v:12:y:2023:i:1:f:12_1_3
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    References listed on IDEAS

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    1. Schmidt, Peter & Sickles, Robin, 1977. "Some Further Evidence on the Use of the Chow Test under Heteroskedasticity," Econometrica, Econometric Society, vol. 45(5), pages 1293-1298, July.
    2. Toyoda, Toshihisa, 1974. "Use of the Chow Test under Heteroscedasticity," Econometrica, Econometric Society, vol. 42(3), pages 601-608, May.
    3. Rachael Meager, 2019. "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 57-91, January.
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    5. Thursby, Jerry G., 1992. "A comparison of several exact and approximate tests for structural shift under heteroscedasticity," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 363-386.
    6. Dwi Kartikasari & Marisa Merianti, 2016. "The Effect of Leverage and Firm Size to Profitability of Public Manufacturing Companies in Indonesia," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 409-413.
    7. Meager, Rachael, 2019. "Understanding the average impact of microcredit expansions: a Bayesian hierarchical analysis of seven randomized experiments," LSE Research Online Documents on Economics 88190, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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