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

Arbeitspapier

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 estimates are moments estimates. They do not require the disturbances to be Gaussian, but if they are, the 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 therefore uses more of the available information for estimating intermediate states.. Further, the VC filter has a clear descriptive interpretation.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IZA Discussion Papers ; No. 12920

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Model Evaluation, Validation, and Selection
• Thema: time-series analysis ; linear model ; state-space estimation ; time-varying coefficients ; moments estimation ; Kalman filtering ; penalized least squares

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Schlicht, Ekkehart
• Ereignis: Veröffentlichung
• (wer): Institute of Labor Economics (IZA)
• (wo): Bonn
• (wann): 2020

Weitere Informationen

• Handle: http://hdl.handle.net/10419/215316
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ