Back to Search Start Over

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

Authors :
Christophe Chesneau
Salima El Kolei
Fabien Navarro
Laboratoire de Mathématiques Nicolas Oresme (LMNO)
Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN)
Normandie Université (NU)-Normandie Université (NU)
Centre de Recherche en Economie et Statistique [Bruz] (CREST)
Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI)
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM)
Université Paris 1 Panthéon-Sorbonne (UP1)
Université de Caen Normandie (UNICAEN)
Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....28daadc31b1efc94a6f41011ddd6297e