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Variance Estimation in a Random Coefficients Model

Author

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  • Schlicht, Ekkehart
  • Ludsteck, Johannes

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.

Suggested Citation

  • Schlicht, Ekkehart & Ludsteck, Johannes, 2006. "Variance Estimation in a Random Coefficients Model," Discussion Papers in Economics 904, University of Munich, Department of Economics.
  • Handle: RePEc:lmu:muenec:904
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    References listed on IDEAS

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    1. 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.
    2. Robert E. Lucas, Jr. & Thomas J. Sargent, 1979. "After Keynesian macroeconomics," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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    1. repec:wly:ijfiec:v:22:y:2017:i:3:p:244-254 is not listed on IDEAS
    2. Baxa, Jaromír & Horváth, Roman & Vašíček, Bořek, 2014. "How Does Monetary Policy Change? Evidence On Inflation-Targeting Countries," Macroeconomic Dynamics, Cambridge University Press, vol. 18(03), pages 593-630, April.
    3. Schlicht, Ekkehart, 2004. "Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter," IZA Discussion Papers 1054, Institute for the Study of Labor (IZA).
    4. Baxa, Jaromír & Horváth, Roman & Vašíček, Bořek, 2013. "Time-varying monetary-policy rules and financial stress: Does financial instability matter for monetary policy?," Journal of Financial Stability, Elsevier, vol. 9(1), pages 117-138.
    5. Jaromír Baxa & Roman Horváth & Bořek Vašíček, 2011. "Time Varying Monetary Policy Rules and Financial Stress," Chapters,in: Handbook of Central Banking, Financial Regulation and Supervision, chapter 10 Edward Elgar Publishing.
    6. Schlicht, Ekkehart, . "Isolation and Aggregation in Economics," Monographs in Economics, University of Munich, Department of Economics, number 38821, November.
    7. António Afonso & João Tovar Jalles, 2016. "Economic Volatility and Sovereign Yields’ Determinants: a Time-Varying Approach," Working Papers Department of Economics 2016/04, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    8. António Afonso & João Tovar Jalles, 2017. "Euro area time‐varying fiscal sustainability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(3), pages 244-254, July.

    More about this item

    Keywords

    time-varying coefficients; adaptive estimation; random walk; Kalman filter; state-space model;

    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; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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