A Data mining approach to indirect inference


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

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

Institut d'Anàlisi Econòmica

Data(s)

02/11/2009

Resumo

Consider a model with parameter phi, and an auxiliary model with parameter theta. Let phi be a randomly sampled from a given density over the known parameter space. Monte Carlo methods can be used to draw simulated data and compute the corresponding estimate of theta, say theta_tilde. A large set of tuples (phi, theta_tilde) can be generated in this manner. Nonparametric methods may be use to fit the function E(phi|theta_tilde=a), using these tuples. It is proposed to estimate phi using the fitted E(phi|theta_tilde=theta_hat), where theta_hat is the auxiliary estimate, using the real sample data. This is a consistent and asymptotically normally distributed estimator, under certain assumptions. Monte Carlo results for dynamic panel data and vector autoregressions show that this estimator can have very attractive small sample properties. Confidence intervals can be constructed using the quantiles of the phi for which theta_tilde is close to theta_hat. Such confidence intervals are found to have very accurate coverage.

Formato

23

434861 bytes

application/pdf

Identificador

http://hdl.handle.net/2072/41986

Idioma(s)

eng

Relação

Working papers; 788.09

Direitos

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Palavras-Chave #Mineria de dades #Anàlisi de dades de panel
Tipo

info:eu-repo/semantics/workingPaper