Likelihood inference on semiparametric models with generated regressors

Matsushita, Yukitoshi and Otsu, Taisuke (2020) Likelihood inference on semiparametric models with generated regressors. Econometric Theory, 36 (4). 626 - 657. ISSN 0266-4666

Text (LIKELIHOOD INFERENCE ON SEMIPARAMETRIC MODELS WITH GENERATED REGRESSORS) - Accepted Version Download (543kB)

• Author

Abstract

Hahn and Ridder (2013, Econometrica 81, 315–340) 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, Econometrica 64, 1263–1297) and propensity score matching estimators for treatment effects by Heckman, Ichimura, and Todd (1998, Review of Economic Studies 65, 261–294). The present article 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, Annals of Statistics , forthcoming) 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
Official URL:	https://www.cambridge.org/core/journals/econometri...
Additional Information:	© 2019 Cambridge University Press
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
Date Deposited:	29 Nov 2019 11:00
Last Modified:	06 Apr 2024 22:45
URI:	http://eprints.lse.ac.uk/id/eprint/102696

Actions (login required)

View Item