Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Michael Creel ()

UFAE and IAE Working Papers from Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC)

Abstract: 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.

Keywords: dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
Date: Written
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Persistent link: http://EconPapers.repec.org/RePEc:aub:autbar:725.08

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