Estimation and Inference of Semiparametric Models Using Data from Several Sources

Moshe Buchinsky (), Fanghua Li and Zhipeng Liao
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Moshe Buchinsky: UCLA - University of California [Los Angeles] - UC - University of California, ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique
Fanghua Li: UNSW - University of New South Wales [Sydney]
Zhipeng Liao: UCLA - University of California [Los Angeles] - UC - University of California

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Abstract: This paper studies the estimation and inference of nonlinear econometric models when the economic variables are contained in different data sets. We construct a semiparametric minimum distance (SMD) estimator of the unknown structural parameter of interest when there are some common conditioning variables in different data sets. The SMD estimator is shown to be consistent and has an asymptotic normal distribution. We provide the explicit form of the optimal weight for the SMD estimation. We provide a consistent estimator of the variance–covariance matrix of the SMD estimator, and hence inference procedures of the unknown parameter vector. The finite sample performances of the SMD estimators and the proposed inference procedures are investigated in few alternative Monte Carlo simulation studies.

Keywords: Conditional moment restrictions; Data combination; Minimum distance estimation; Series estimation (search for similar items in EconPapers)
Date: 2022-01
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Citations: View citations in EconPapers (3)

Published in Journal of Econometrics, 2022, 226 (1), pp.80-103. ⟨10.1016/j.jeconom.2020.10.011⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03926721

DOI: 10.1016/j.jeconom.2020.10.011

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