Variance Estimation in a Random Coefficients Model

33 Pages Posted: 24 Mar 2006

See all articles by Ekkehart Schlicht

Ekkehart Schlicht

University of Munich - Department of Economics; IZA Institute of Labor Economics

Johannes Ludsteck

Government of the Federal Republic of Germany - Institute for Employment Research (IAB)

Date Written: March 2006

Abstract

This papers describes an estimator for a standard state-space model with coefficients generated by a random walk that is statistically superior to the Kalman filter as applied to this particular class of models. Two closely related estimators for the variances are introduced: A maximum likelihood estimator and a moments estimator that builds on the idea that some moments are equalized to their expectations. These estimators perform quite similar in many cases. In some cases, however, the moments estimator is preferable both to the proposed likelihood estimator and the Kalman filter, as implemented in the program package Eviews.

Keywords: time-varying coefficients, adaptive estimation, Kalman filter, state-space

JEL Classification: C2, C22, C51, C52

Suggested Citation

Schlicht, Ekkehart and Ludsteck, Johannes, Variance Estimation in a Random Coefficients Model (March 2006). IZA Discussion Paper No. 2031. Available at SSRN: https://ssrn.com/abstract=892824

Ekkehart Schlicht (Contact Author)

University of Munich - Department of Economics ( email )

Ludwigstrasse 28
Munich, D-80539
Germany

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Johannes Ludsteck

Government of the Federal Republic of Germany - Institute for Employment Research (IAB) ( email )

Regensburger Str. 104
Nuremberg, 90478
Germany

Register to save articles to
your library

Register

Paper statistics

Downloads
147
Abstract Views
836
rank
200,532
PlumX Metrics