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Variance Estimation in a Random Coefficients Model

Author

Listed:
  • Schlicht, Ekkehart

    (University of Munich)

  • Ludsteck, Johannes

    (Institut für Arbeitsmarkt- und Berufsforschung)

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," IZA Discussion Papers 2031, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2031
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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 & Thomas J. Sargent, 1979. "After Keynesian macroeconomics," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 3(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.
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    Cited by:

    1. 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.
    2. Sangyup Choi & Davide Furceri & João Tovar Jalles, 2022. "Heterogeneous gains from countercyclical fiscal policy: new evidence from international industry-level data [Optimal investment with costly reversibility]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 773-804.
    3. João T. Jalles, 2022. "Do credit rating agencies reward fiscal prudence?," International Finance, Wiley Blackwell, vol. 25(1), pages 2-22, April.
    4. Baxa, Jaromír & Horváth, Roman & Vašíček, Bořek, 2014. "How Does Monetary Policy Change? Evidence On Inflation-Targeting Countries," Macroeconomic Dynamics, Cambridge University Press, vol. 18(3), pages 593-630, April.
    5. António Afonso & João Tovar Jalles, 2020. "Economic volatility and sovereign yields’ determinants: a time-varying approach," Empirical Economics, Springer, vol. 58(2), pages 427-451, February.
    6. 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.
    7. Schlicht, Ekkehart, . "Isolation and Aggregation in Economics," Monographs in Economics, University of Munich, Department of Economics, number 38821, November.
    8. Davide Furceri & João Tovar Jalles & Prakash Loungani, 2020. "On the Determinants of the Okun’s Law: New Evidence from Time-Varying Estimates," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 62(4), pages 661-700, December.
    9. Jalles, João Tovar, 2020. "Social expenditure cyclicality: New time-varying evidence in developing economies," Economic Systems, Elsevier, vol. 44(3).
    10. Jalles, João Tovar, 2021. "Dynamics of government spending cyclicality," Economic Modelling, Elsevier, vol. 97(C), pages 411-427.
    11. Schlicht, Ekkehart, 2004. "Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter," IZA Discussion Papers 1054, Institute of Labor Economics (IZA).
    12. Jalles, João Tovar, 2020. "The volatility impact of social expenditure’s cyclicality in advanced economies," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 26-40.
    13. Jaromír Baxa & Roman Horváth & Bořek Vašíček, 2011. "Time Varying Monetary Policy Rules and Financial Stress," Chapters, in: Sylvester Eijffinger & Donato Masciandaro (ed.), Handbook of Central Banking, Financial Regulation and Supervision, chapter 10, Edward Elgar Publishing.
    14. Reginaldo Pinto Nogueira, 2009. "Testing credibility with time-varying coefficients," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1813-1817.
    15. João T. Jalles, 2020. "Explaining Africa's public consumption procyclicality: Revisiting old evidence," International Finance, Wiley Blackwell, vol. 23(2), pages 297-323, August.
    16. João Tovar Jalles, 2019. "On the Time‐Varying Relationship between Unemployment and Output: What shapes it?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(5), pages 605-630, November.

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    More about this item

    Keywords

    time-varying coefficients; adaptive estimation; Kalman filter; state-space;
    All these keywords.

    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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