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Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/35486
Title: Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments
Authors: Creel, Michael; Kristensen, Dennis
Keywords: Dynamic latent variable models
Simulation-based estimation
Simulated moments
Kernel regression
Nonparametric estimation
Issue Date: 12-Nov-2009
Citation: UFAE and IAE Working Papers ; 792.09
Abstract: 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. Because 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. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.
Description: JEL codes: C13; C14; C15
Publisher version (URL): http://pareto.uab.es/wp/2009/79209.pdf
URI: http://hdl.handle.net/10261/35486
Appears in Collections:(IAE) Informes y documentos de trabajo
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