Estimation of dynamic latent variable models using simulated nonparametric moments


Autoria(s): Creel, Michael D.
Contribuinte(s)

Universitat Autònoma de Barcelona. Unitat de Fonaments de l'Anàlisi Econòmica

Institut d'Anàlisi Econòmica

Data(s)

03/04/2008

Resumo

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

Formato

19

164994 bytes

application/pdf

Identificador

http://hdl.handle.net/2072/5289

Idioma(s)

eng

Relação

Working papers; 725.08

Direitos

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Palavras-Chave #Estimació, Teoria de l' #Estadística no paramètrica
Tipo

info:eu-repo/semantics/workingPaper