Parametric estimation of hidden Markov models by least squares type estimation and deconvolution

Christophe Chesneau (), Salima El Kolei () and Fabien Navarro ()
Additional contact information
Christophe Chesneau: Université de Caen; LMNO
Salima El Kolei: CREST;ENSAI
Fabien Navarro: CREST; ENSAI

No 2017-66, Working Papers from Center for Research in Economics and Statistics

Abstract: In this paper, we study a speci?c hidden Markov chain de?ned by the equation: Yi = Xi + ei, i = 1,...,n + 1, where (Xi)i=1 is a real-valued stationary Markov chain and (ei)i=1 is a noise independent of (Xi)i=1. We develop a new parametric approach obtained by minimization of a particular contrast taking advantage of the regressive problem and based on deconvolution strategy. We provide theoretical guarantees on the performance of the resulting estimator; its consistency and its asymptotic normality are established.

Keywords: Contrast function; deconvolution; least square estimation; parametric inference; stochastic volatility (search for similar items in EconPapers)
Pages: 13 pages
Date: 2017
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://crest.science/RePEc/wpstorage/2017-66.pdf CREST working paper version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:crs:wpaper:2017-66

Access Statistics for this paper

More papers in Working Papers from Center for Research in Economics and Statistics Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Murielle Jules Maintainer-Email : murielle.jules@ensae.Fr.