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Efficient Estimation of the Parameter Path in Unstable Time Series Models

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  • Mueller, Ulrich
  • Petalas, Philippe-Emmanuel
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    Abstract

    The paper investigates asymptotically efficient inference in general likelihood models with time varying parameters. Parameter path estimators and tests of parameter constancy are evaluated by their weighted average risk and weighted average power, respectively. The weight function is proportional to the distribution of a Gaussian process, and focusses on local parameter instabilities that cannot be detected with certainty even in the limit. It is shown that asymptotically, the sample information about the parameter path is efficiently summarized by a Gaussian pseudo model. This approximation leads to computationally convenient formulas for efficient path estimators and test statistics, and unifies the theory of stability testing and parameter path estimation.

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    File URL: http://mpra.ub.uni-muenchen.de/2260/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2260.

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    Date of creation: Mar 2007
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    Handle: RePEc:pra:mprapa:2260

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    Related research

    Keywords: Time Varying Parameters; Non-linear Non-Gaussian Smoothing; Weighted Average Risk; Weighted Average Power; Posterior Approximation; Contiguity;

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    1. Andrews, Donald W.K., 1992. "Generic Uniform Convergence," Econometric Theory, Cambridge University Press, vol. 8(02), pages 241-257, June.
    2. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
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