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Structural change tests based on implied probabilities for GEL criteria

Author

Listed:
  • Alain Guay

    (Department of Economics, Universite du Quebec a Montreal)

  • Jean-Francois Lamarche

    (Department of Economics, Brock University)

Abstract

This paper proposes Pearson-type statistics based on implied probabilities to detect structural change. The class of generalized empirical likelihood estimators (see Smith (1997)) assigns a set of implied probabilities to each observation such that moment conditions are satisfied. The proposed test statistics for structural change are based on the information content in these implied probabilities. We consider cases of structural change with unknown breakpoint which can occur in the parameters of interest or in the overidentifying restrictions used to estimate these parameters. We also propose a structural change test based on implied probabilities that is robust to weak identification or cases in which parameters are completely unidentified. The test statistics considered here have competitive size and power properties. Moreover, they are computed in a single step which eliminates the need to compute the weighting matrix required for GMM estimation.

Suggested Citation

  • Alain Guay & Jean-Francois Lamarche, 2009. "Structural change tests based on implied probabilities for GEL criteria," Working Papers 0904, Brock University, Department of Economics, revised May 2011.
    • Handle: RePEc:brk:wpaper:0904
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    Keywords

    Generalized empirical likelihood; generalized method of moments; parameter instability; structural change;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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