Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models

Mark W. Watson, Robert F. Engle

Research output: Contribution to journalArticle

227 Scopus citations

Abstract

This paper provides a general approach to the formulation and estimation of dynamic unobserved component models. After introducing the general model, two methods for estimating the unknown parameters are presented. Both are algorithms for maximizing the likelihood function. The first is based on the method of Scoring. The second is the EM algorithm, a derivative-free method. Each iteration of EM requires a Kalman filter and smoother followed by straightforward regression calculations. The paper suggests using the EM methods to quickly locate a neighborhood of the maximum. Scoring can then be used to pinpoint the maximum and calculate the information matrix.

Original languageEnglish (US)
Pages (from-to)385-400
Number of pages16
JournalJournal of Econometrics
Volume23
Issue number3
DOIs
StatePublished - Jan 1 1983
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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