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Break Date Estimation for Models with Deterministic Structural Change

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  • David I. Harvey
  • Stephen J. Leybourne

Abstract

type="main" xml:id="obes12037-abs-0001"> In this article, we consider estimating the timing of a break in level and/or trend when the order of integration and autocorrelation properties of the data are unknown. For stationary innovations, break point estimation is commonly performed by minimizing the sum of squared residuals across all candidate break points, using a regression of the levels of the series on the assumed deterministic components. For unit root processes, the obvious modification is to use a first differenced version of the regression, while a further alternative in a stationary autoregressive setting is to consider a GLS-type quasi-differenced regression. Given uncertainty over which of these approaches to adopt in practice, we develop a hybrid break fraction estimator that selects from the levels-based estimator, the first-difference-based estimator, and a range of quasi-difference-based estimators, according to which achieves the global minimum sum of squared residuals. We establish the asymptotic properties of the estimators considered, and compare their performance in practically relevant sample sizes using simulation. We find that the new hybrid estimator has desirable asymptotic properties and performs very well in finite samples, providing a reliable approach to break date estimation without requiring decisions to be made regarding the autocorrelation properties of the data.

Suggested Citation

  • David I. Harvey & Stephen J. Leybourne, 2014. "Break Date Estimation for Models with Deterministic Structural Change," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 623-642, October.
    • Handle: RePEc:bla:obuest:v:76:y:2014:i:5:p:623-642
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    File URL: http://hdl.handle.net/10.1111/obes.2014.76.issue-5
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    Cited by:

    1. Skrobotov Anton, 2018. "On Trend Breaks and Initial Condition in Unit Root Testing," Journal of Time Series Econometrics, De Gruyter, vol. 10(1), pages 1-15, January.
    2. repec:rnp:ppaper:mak6 is not listed on IDEAS
    3. Harvey, David I. & Leybourne, Stephen J., 2015. "Confidence sets for the date of a break in level and trend when the order of integration is unknown," Journal of Econometrics, Elsevier, vol. 184(2), pages 262-279.
    4. Skrobotov Anton, 2013. "Bias Correction of KPSS Test with Structural Break for Reducing of Size Distortion," Journal of Time Series Econometrics, De Gruyter, vol. 6(1), pages 33-61, December.
    5. Anton Skrobotov, 2020. "Survey on structural breaks and unit root tests," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 96-141.
    6. Ioanna Konstantakopoulou, 2017. "The aggregate exports-GDP relation under the prism of infrequent trend breaks and multi-horizon causality," International Economics and Economic Policy, Springer, vol. 14(4), pages 661-689, October.
    7. Yiannis Karavias & Elias Tzavalis, 2014. "Testing for unit roots in panels with structural changes, spatial and temporal dependence when the time dimension is finite," Discussion Papers 14/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    8. Harvey, David I. & Leybourne, Stephen J., 2016. "Improving the length of confidence sets for the date of a break in level and trend when the order of integration is unknown," Economics Letters, Elsevier, vol. 145(C), pages 239-245.
    9. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.
    10. Skrobotov Anton, 2018. "On Trend Breaks and Initial Condition in Unit Root Testing," Journal of Time Series Econometrics, De Gruyter, vol. 10(1), pages 1-15, January.

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