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Valid Inference in Partially Unstable GMM Models

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  • Li, Hong
  • Mueller, Ulrich
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    Abstract

    The paper considers time series GMM models where a subset of the parameters are time varying. The magnitude of the time variation in the unstable parameters is such that efficient tests detect the instability with (possibly high) probability smaller than one, even in the limit. We show that for many forms of the instability and a large class of GMM models, standard GMM inference on the subset of stable parameters, ignoring the partial instability, remains asymptotically valid.

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    File URL: http://mpra.ub.uni-muenchen.de/2261/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2261.

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    Date of creation: Aug 2006
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    Handle: RePEc:pra:mprapa:2261

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    Related research

    Keywords: Structural Breaks; Parameter Stability Test; Contiguity; Euler Condition; New Keynesian Phillips Curve;

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    1. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
    2. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
    3. Andrews, Donald W.K., 1992. "Generic Uniform Convergence," Econometric Theory, Cambridge University Press, vol. 8(02), pages 241-257, June.
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    Cited by:
    1. Li, Hong, 2008. "Estimation and testing of Euler equation models with time-varying reduced-form coefficients," Journal of Econometrics, Elsevier, vol. 142(1), pages 425-448, January.

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