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Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

  • Michael Creel

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    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.

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    File URL: http://pareto.uab.es/wp/2009/79209.pdf
    File Function: New and extended version joint with Dennis Kristensen (See WP 792.09 in this series - November 12, 2009)
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    Paper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 725.08.

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    Length: 19
    Date of creation: 13 Feb 2008
    Date of revision: 02 Jun 2008
    Handle: RePEc:aub:autbar:725.08
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