Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/17736
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Título : A Data Mining Approach to Indirect Inference
Autor : Creel, Michael
Palabras clave : Simulation-based estimation
Data mining
Dynamic panel data
Vector autoregression
Bias reduction Abstract JEL codes
C13
C14
C15
C33
Fecha de publicación : 13-Oct-2009
Citación : UFAE and IAE Working Papers ; 788.09
Resumen: 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.
Descripción : 23 pages, 7 tables, 8 figures.
Versión del editor: http://pareto.uab.es/wp/2009/78809.pdf
URI : http://hdl.handle.net/10261/17736
Appears in Collections:(IAE) Informes y documentos de trabajo

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