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Confidence Sets for the Break Date in Cointegrating Regressions

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  • KUROZUMI, Eiji
  • SKROBOTOV, Anton

Abstract

In this paper, we propose constructing confidence sets for a break date in cointegrating regressions by inverting a test for the break location, which is obtained by maximizing the weighted average of power. It is found that the limiting distribution of the test depends on the number of I(1) regressors whose coefficients sustain structural change and the number of I(1) regressors whose coefficients are fixed throughout the sample. By Monte Carlo simulations, we then show that compared with a confidence interval developed by using the existing method based on the limiting distribution of the break point estimator under the assumption of the shrinking shift, the confidence set proposed in the present paper has a more accurate coverage rate, while the length of the confidence set is comparable. By using the method developed in this paper, we then investigate the cointegrating regressions of Russian macroeconomic variables with oil prices with a break.

Suggested Citation

  • KUROZUMI, Eiji & SKROBOTOV, Anton, 2016. "Confidence Sets for the Break Date in Cointegrating Regressions," Discussion Papers 2016-07, Graduate School of Economics, Hitotsubashi University.
  • Handle: RePEc:hit:econdp:2016-07
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    Cited by:

    1. Andrey Feliksovich Bedin & Alexander Vladimirovich Kulikov & Andrey Vladimirovich Polbin, 2021. "A Markov Switching VECM Model for Russian Real GDP, Real Exchange Rate and Oil Prices," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 402-412.

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    More about this item

    Keywords

    Confidence interval; structural change; cointegration; Russian economy; oil price;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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