Efficient Estimation of the Parameter Path in Unstable Time Series Models

Ulrich Mueller and Philippe-Emmanuel Petalas

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Time Varying Parameters; Non-linear Non-Gaussian Smoothing; Weighted Average Risk; Weighted Average Power; Posterior Approximation; Contiguity (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2007-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:2260

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