Variance Estimation in a Random Coefficients Model
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.
|Date of creation:||Mar 2006|
|Contact details of provider:|| Postal: Ludwigstr. 28, 80539 Munich, Germany|
Web page: http://www.vwl.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Robert E. Lucas, Jr. & Thomas J. Sargent, 1979. "After Keynesian macroeconomics," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr.
- Michael Athans, 1974. "The Importance of Kalman Filtering Methods for Economic Systems," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 3, number 1, pages 49-64 National Bureau of Economic Research, Inc.
- Cooley, Thomas F & Prescott, Edward C, 1973. "An Adaptive Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 364-371, June.