Efficient Estimation of the Parameter Path in Unstable Time Series Models
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.
|Date of creation:||Mar 2007|
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- Andrews, Donald W.K., 1992.
"Generic Uniform Convergence,"
Cambridge University Press, vol. 8(02), pages 241-257, June.
- Andrews, Donald W K, 1993.
"Tests for Parameter Instability and Structural Change with Unknown Change Point,"
Econometric Society, vol. 61(4), pages 821-56, July.
- 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.
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