This paper analyzes the MSE of the exponentially weighted least squares (EWLS) estimator in dynamic regression modelswith time-varying parameters. Under the assumption of differentiable parameter functions, it is derived an asymptotic expression which is the sum of a stationary and of an evolutionary component. The validity of the analytical expression is illustrated with simulation experiments, and its usefulness in designing the exponential discounting factor is illustrated on a real case-study. The practical finding is similar to the plug-in bandwidth selection in nonparametric smoothers.

Performance of Adaptive Estimators in Slowly-Varying Parameter Models

GRILLENZONI, CARLO
2008-01-01

Abstract

This paper analyzes the MSE of the exponentially weighted least squares (EWLS) estimator in dynamic regression modelswith time-varying parameters. Under the assumption of differentiable parameter functions, it is derived an asymptotic expression which is the sum of a stationary and of an evolutionary component. The validity of the analytical expression is illustrated with simulation experiments, and its usefulness in designing the exponential discounting factor is illustrated on a real case-study. The practical finding is similar to the plug-in bandwidth selection in nonparametric smoothers.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11578/192
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