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Variance Estimation in a Random Coefficients Model

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Author Info
Ekkehart Schlicht () (University of Munich and IZA Bonn)
Johannes Ludsteck () (Institute for Employment Research (IAB))

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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.

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Publisher Info
Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2031.

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Length: 33 pages
Date of creation: Mar 2006
Date of revision:
Handle: RePEc:iza:izadps:dp2031

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Related research
Keywords: time-varying coefficients; adaptive estimation; Kalman filter; state-space;

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Find related papers by JEL classification:
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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References listed on IDEAS
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.:
  1. Robert E. Lucas, Jr. & Thomas J. Sargent, 1979. "After Keynesian macroeconomics," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr. [Downloadable!]
  2. 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. [Downloadable!]
  3. 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 364-71, June. [Downloadable!] (restricted)
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Cited by:
(explanations, 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.)

  1. Schlicht, Ekkehart, 2004. "Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter," Discussion Papers in Economics 304, University of Munich, Department of Economics. [Downloadable!]
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This page was last updated on 2009-11-23.

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