VC: a method for estimating time-varying coefficients in linear models

Artikel

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. The method calculates the conditional expectations of the coefficients, given the observations. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The 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 filter proposed here is two sided and therefore uses more of the available information for estimating intermediate states. Further, the proposed filter has a clear descriptive interpretation.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of the Korean Statistical Society ; ISSN: 2005-2863 ; Volume: 50 ; Year: 2021 ; Issue: 4 ; Pages: 1164-1196 ; Singapore: Springer

Classification and Topics

• Classification: Mathematik ; 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
• Subject: Time-series analysis ; Linear model ; State-space estimation ; Time-varying coefficients ; Moments estimation. Kalman filtering ; Penalized least squares ; HP-Filter

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schlicht, Ekkehart
• Event: Veröffentlichung
• (who): Springer
• (where): Singapore
• (when): 2021

Further information

• DOI: doi:10.1007/s42952-021-00110-y
• Last update: 10.03.2025, 11:42 AM CET