Inference in models with adaptive learning
Guillaume Chevillon (),
Michael Massmann and
Journal of Monetary Economics, 2010, vol. 57, issue 3, 341-351
Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be conducted using the Anderson-Rubin statistic with appropriate choice of instruments. Application of this method to a typical new Keynesian sticky-price model with perpetual learning demonstrates its usefulness in practice.
Keywords: Weak; identification; Persistence; Anderson-Rubin; statistic; DSGE; models (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:moneco:v:57:y:2010:i:3:p:341-351
Access Statistics for this article
Journal of Monetary Economics is currently edited by R. G. King and C. I. Plosser
More articles in Journal of Monetary Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().