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Uncovering Time-Varying Parameters with the Kalman-Filter and the Flexible Least Squares: a Monte Carlo Study

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  • Zsolt Darvas
  • Balázs Varga

    (OTP Fund Management and Corvinus University of Budapest)

Abstract

Using Monte Carlo methods, we compare the ability of the Kalman-filter, the Kalman-smoother and the flexible least squares (FLS) to uncover the parameters of an autoregression. We find that the ordinary least squares (OLS) estimator performs much better that the time-varying coefficient methods when the parameters are in fact constant, but the OLS does very poorly when parameters change. Neither the FLS, nor the Kalman-filter and Kalman-smoother can uncover sudden changes in parameters. But when parameter changes are smoother, such as linear, sinusoid or even random walk changes in the parameters, the FLS with a weight parameter 100 works reasonably well and typically outperforms even the Kalman-smoother, which is in turn performed better than the Kalman-filter.

Suggested Citation

  • Zsolt Darvas & Balázs Varga, 2012. "Uncovering Time-Varying Parameters with the Kalman-Filter and the Flexible Least Squares: a Monte Carlo Study," Working Papers 1204, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    • Handle: RePEc:mkg:wpaper:1204
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    References listed on IDEAS

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    1. Kalaba, Robert & Tesfatsion, Leigh, 1988. "The flexible least squares approach to time-varying linear regression," Journal of Economic Dynamics and Control, Elsevier, vol. 12(1), pages 43-48, March.
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    3. Kalaba, Robert E. & Tesfatsion, Leigh S., 1990. "Flexible Least Squares for Approximately Linear Systems," Staff General Research Papers Archive 11190, Iowa State University, Department of Economics.
    4. Kalaba, Robert & Tesfatsion, Leigh, 1996. "A multicriteria approach to model specification and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 21(2), pages 193-214, February.
    5. Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.
    6. Kalaba, Robert E. & Tesfatsion, Leigh S., 1989. "Time-Varying Linear Regression Via Flexible Least Squares," Staff General Research Papers Archive 11196, Iowa State University, Department of Economics.
    7. Granger Clive W.J., 2008. "Non-Linear Models: Where Do We Go Next - Time Varying Parameter Models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-11, September.
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    Citations

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    Cited by:

    1. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    2. Alptekin, Aynur & Broadstock, David C. & Chen, Xiaoqi & Wang, Dong, 2019. "Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions," Energy Economics, Elsevier, vol. 82(C), pages 26-41.
    3. Evžen Kočenda & Balázs Varga, 2018. "The Impact of Monetary Strategies on Inflation Persistence," International Journal of Central Banking, International Journal of Central Banking, vol. 14(4), pages 229-274, September.
    4. Scharnagl, Michael & Stapf, Jelena, 2015. "Inflation, deflation, and uncertainty: What drives euro-area option-implied inflation expectations, and are they still anchored in the sovereign debt crisis?," Economic Modelling, Elsevier, vol. 48(C), pages 248-269.
    5. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    6. Zsolt Darvas & Balẳ Varga, 2014. "Inflation persistence in central and eastern European countries," Applied Economics, Taylor & Francis Journals, vol. 46(13), pages 1437-1448, May.
    7. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    8. Scharnagl, Michael & Stapf, Jelena, 2014. "Inflation, deflation, and uncertainty: What drives euro area option-implied inflation expectations and are they still anchored in the sovereign debt crisis?," Discussion Papers 24/2014, Deutsche Bundesbank.
    9. A. Talha Yalta, 2016. "Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 339-366, August.

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    More about this item

    Keywords

    flexible least squares; Kalman-filter; time-varying coefficient models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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