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Testing for common breaks in a multiple equations system

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  • Tatsushi Oka

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  • Pierre Perron

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Abstract

The issue addressed in this paper is that of testing for common breaks across or within equations of a multivariate system. Our framework is very general and allows integrated regressors and trends as well as stationary regressors. The null hypothesis is that breaks in different parameters occur at common locations and are separated by some positive fraction of the sample size unless they occur across different equations. Under the alternative hypothesis, the break dates across parameters are not the same and also need not be separated by a positive fraction of the sample size whether within or across equations. The test considered is the quasi-likelihood ratio test assuming normal errors, though as usual the limit distribution of the test remains valid with non-normal errors. Of independent interest, we provide results about the rate of convergence of the estimates when searching over all possible partitions subject only to the requirement that each regime contains at least as many observations as some positive fraction of the sample size, allowing break dates not separated by a positive fraction of the sample size across equations. Simulations show that the test has good finite sample properties. We also provide an application to issues related to level shifts and persistence for various measures of inflation to illustrate its usefulness.

Suggested Citation

  • Tatsushi Oka & Pierre Perron, 2018. "Testing for common breaks in a multiple equations system," Monash Econometrics and Business Statistics Working Papers 3/18, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2018-3
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp03-2018.pdf
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    References listed on IDEAS

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    Cited by:

    1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    2. Ye Li & Pierre Perron, 2013. "Inference Related to Locally Ordered and Common Breaks in a Multivariate System with Joined Segmented Trends," Boston University - Department of Economics - Working Papers Series 2013-010, Boston University - Department of Economics.
    3. Kim, Dukpa & Oka, Tatsushi & Estrada, Francisco & Perron, Pierre, 2020. "Inference related to common breaks in a multivariate system with joined segmented trends with applications to global and hemispheric temperatures," Journal of Econometrics, Elsevier, vol. 214(1), pages 130-152.
    4. Karsten Schweikert, 2020. "Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions," Papers 2001.07949, arXiv.org, revised Nov 2020.
    5. Manner, Hans & Blatt, Dominik & Candelon, Bertrand, 2014. "Detecting financial contagion in a multivariate system," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100411, Verein für Socialpolitik / German Economic Association.
    6. Yunjong Eo & James Morley, 2015. "Likelihood‐ratio‐based confidence sets for the timing of structural breaks," Quantitative Economics, Econometric Society, vol. 6(2), pages 463-497, July.
    7. Bergamelli, Michele & Bianchi, Annamaria & Khalaf, Lynda & Urga, Giovanni, 2019. "Combining p-values to test for multiple structural breaks in cointegrated regressions," Journal of Econometrics, Elsevier, vol. 211(2), pages 461-482.
    8. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    9. Richard S. J. Tol & Francisco Estrada & Carlos Gay-García, 2012. "The persistence of shocks in GDP and the estimation of the potential economic costs of climate change," Working Paper Series 4312, Department of Economics, University of Sussex Business School.
    10. Pierre L. Siklos, 2020. "Looking into the Rear-View Mirror: Lessons from Japan for the Eurozone and the U.S?," IMES Discussion Paper Series 20-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    11. Alexander Berglund & Massimo Guidolin & Manuela Pedio, 2020. "Monetary policy after the crisis: A threat to hedge funds' alphas?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 219-238, May.
    12. Ye Li & Pierre Perron, 2017. "Inference on locally ordered breaks in multiple regressions," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 289-353, March.
    13. Blatt, Dominik & Candelon, Bertrand & Manner, Hans, 2015. "Detecting contagion in a multivariate time series system: An application to sovereign bond markets in Europe," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 1-13.

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    More about this item

    Keywords

    change-point; segmented regressions; break dates; hypothesis testing; multiple equations systems.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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