Efficient estimation of the parameter path in unstable time series models

Ulrich K. Müller, Philippe Emmanuel Petalas

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

The paper investigates inference in non-linear and non-Gaussian models with moderately time-varying parameters. We show that for many decision problems, the sample information about the parameter path can be summarized by an artificial linear and Gaussian model, at least asymptotically. The approximation allows for computationally convenient path estimators and parameter stability tests. Also, in contrast to standard Bayesian techniques, the artificial model can be robustified so that in misspecified models, decisions about the path of the (pseudo-true) parameter remain as good as in a corresponding correctly specified model.

Original languageEnglish (US)
Pages (from-to)1508-1539
Number of pages32
JournalReview of Economic Studies
Volume77
Issue number4
DOIs
StatePublished - Oct 2010

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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