An Alternative Estimation Method for Time-Varying Parameter Models

Mikio Itou, Akihiko Noda, Tatsuma Wada

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A multivariate, non-Bayesian, regression-based, or feasible generalized least squares (GLS)-based approach is proposed to estimate time-varying VAR parameter models. Although it has been known that the Kalman-smoothed estimate can be alternatively estimated using GLS for univariate models, we assess the accuracy of the feasible GLS estimator compared with commonly used Bayesian estimators. Unlike the maximum likelihood estimator often used together with the Kalman filter, it is shown that the possibility of the pile-up problem occurring is negligible. In addition, this approach enables us to deal with stochastic volatility models, models with a time-dependent variance–covariance matrix, and models with non-Gaussian errors that allow us to deal with abrupt changes or structural breaks in time-varying parameters.

Original languageEnglish
Article number23
Issue number2
Publication statusPublished - 2022 Jun


  • generalized least squares
  • Kalman filter
  • non-Bayesian time-varying model
  • vector autoregressive model

ASJC Scopus subject areas

  • Economics and Econometrics


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