Efficient Estimation of the Parameter Path in Unstable Time Series Models
نویسنده
چکیده
The paper investigates asymptotically efficient inference in general time series likelihood models with time varying parameters. Inference procedures for general loss functions are evaluated by a weighted average risk criterion. The weight function focusses on persistent parameter paths of moderate magnitude, and is proportional to the distribution function of a Gaussian random walk. It is shown that asymptotically efficient inference is equivalent to efficient inference in a Gaussian local level model. By implication, estimators of the parameter path and tests of parameter stability are integrated in one unified asymptotic framework. In practice, efficient estimators and test statistics can hence easily be obtained by variants of Kalman smoothing. JEL Classification: C22, C13, C12, C11
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