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Efficient Estimation and Inference in Cointegrating Regressions with Structural Change

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  • Kurozumi, Eiji
  • Arai, Yoichi

Abstract

This paper investigates an efficient estimation method for a cointegrating regression model with structural change. Our proposal is that we first estimate the break point by minimizing the sum of squared residuals and then, by replacing the break fraction with the estimated one, we estimate the regression model by the canonical cointegrating regression (CCR) method proposed by Park (1992). We show that the estimator of the break fraction is consistent and of order faster than T -1/2 and that the CCR estimator with the estimated break fraction has the same asymptotic property as the estimator with the known break point. Simulation experiments show how the finite sample distribution gets close to the limiting distribution as the magnitude of the break and/or the sample size increases.

Suggested Citation

  • Kurozumi, Eiji & Arai, Yoichi, 2005. "Efficient Estimation and Inference in Cointegrating Regressions with Structural Change," Discussion Papers 2004-09, Graduate School of Economics, Hitotsubashi University.
  • Handle: RePEc:hit:econdp:2004-09
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    References listed on IDEAS

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    Cited by:

    1. Yoichi Arai & Eiji Kurozumi, 2005. "Testing for the Null Hypothesis of Cointegration with Structural Breaks," CIRJE F-Series CIRJE-F-319, CIRJE, Faculty of Economics, University of Tokyo.
    2. David Neto, 2015. "Testing for and dating structural break in smooth time-varying cointegration parameters, with an application to retail gasoline price and crude oil price long-run relationship," Empirical Economics, Springer, vol. 49(3), pages 909-928, November.
    3. Yoichi Arai & Eiji Kurozumi, 2005. "Testing for the Null Hypothesis of Cointegration with Structural Breaks (Subsequently published in "Econometric Reviews", Volume 26, Issue 6 November 2007, pages 705 - 739. )," CARF F-Series CARF-F-022, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

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