VC - A Method For Estimating Time-Varying Coefficients in Linear Models

Schlicht, Ekkehart (November 2019): VC - A Method For Estimating Time-Varying Coefficients in Linear Models. Discussion Papers in Economics 2019-3

Abstract

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The VC estimator is a moments estimator that does not require the disturbances be Gaussian, but if they are, its estimates are asymptotically equivalent to maximum likelihood estimates. In contrast to Kalman filtering, no specification of an initial state or an initial covariance matrix is required. While the Kalman filter is one- sided, the VC filter is two-sided and uses more of the available information for estimating intermediate states.. Further, the VC filter has a clear descriptive interpretation.

The final Version appeared in the Journal of the Korean Statistical Society volume 50, pages 1164–1196 (2021), available at https://link.springer.com/article/10.1007/s42952-021-00110-y

• Item Type: Paper
• Form of publication: Submitted Version
• Keywords: Time-series analysis; linear model; state-space estimation; time-varying coefficients; moments estimation; Kalman filtering; penalized least squares.
• Faculties: Economics; Economics > Munich Discussion Papers in Economics; Economics > Munich Discussion Papers in Economics > Mathematical Methods; Economics > Munich Discussion Papers in Economics > Statistical Methods; Economics > Chairs > Chair of Institutional Economics (closed)
• Subjects: 300 Social sciences > 300 Social sciences, sociology and anthropology; 300 Social sciences > 330 Economics
• JEL Classification: C2, C22, C51, C52
• Language: English

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