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

Zsolt Darvas and Balázs Varga
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Balázs Varga: OTP Fund Management and Corvinus University of Budapest

No 1204, Working Papers from Department of Mathematical Economics and Economic Analysis, 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.

Keywords: flexible least squares; Kalman-filter; time-varying coefficient models (search for similar items in EconPapers)
JEL-codes: C22 E31 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2012-12
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Citations: View citations in EconPapers (10)

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