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Likelihood inference on semiparametric models with generated regressors

Matsushita, Yukitoshi and Otsu, Taisuke (2019) Likelihood inference on semiparametric models with generated regressors. Econometric Theory. ISSN 0266-4666 (In Press)

[img] Text (LIKELIHOOD INFERENCE ON SEMIPARAMETRIC MODELS WITH GENERATED REGRESSORS) - Accepted Version
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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 esti- mators 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). The present paper studies a nonparametric likelihood-based inference method for the parameters in such three step estimation problems. In particular, we apply the general empirical likelihood theory of Bravo, Escanciano and van Keilegom (2018) to modify semiparametric moment functions to account for influences from plug-in estimates into the above important setup, and show that the resulting likelihood ratio statistic becomes asymptotically pivotal without undersmoothing in the first and second step nonparametric estimates.

Item Type: Article
Divisions: Economics
Date Deposited: 29 Nov 2019 11:00
Last Modified: 25 Jan 2020 00:14
URI: http://eprints.lse.ac.uk/id/eprint/102696

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