Likelihood inference on semiparametric models with generated regressors

Yukitoshi Matsushita and Taisuke Otsu

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: Hahn and Ridder (2013) formulated influence functions of semiparametric three step estimators where generated regressors are computed in the first step. This class of estimators covers several important examples for empirical analysis, such as production function estimators by Olley and Pakes (1996), and propensity score matching estimators for treatment effects by Heckman, Ichimura and Todd (1998). This paper develops a nonparametric likelihood- based inference method for the parameters in such three step estimation problems. By modifying the moment functions to account for influences from the first and second step estimation, the resulting likelihood ratio statistic becomes asymptotically pivotal not only without estimating the asymptotic variance but also without undersmoothing.

Keywords: generated regressor; empirical likelihood (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2016-09
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sog
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:587

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