Identification and estimation of semiparametric two‐step models

By Juan-Carlos Escanciano, David T. Jacho-Chávez, Arthur Lewbel. 2016

64 citations recorded by the source corpus.

Let H 0 (X) be a function that can be nonparametrically estimated. Suppose E [Y&7CX]=F 0 [X⊤β 0 , H 0 (X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β 0 and unknown function F 0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional forms. We propose an estimator with asymptotic properties allowing for data dependent bandwidths and random trimming. A Monte Carlo experiment and an empirical application to migration decisions are also included.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/68e9cd3e1e72956b2af60baea281d9b9ee4caeac

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

OLS and IV estimation of regression models including endogenous interaction terms (2019, background): “It can therefore also be interpreted as a parametric implementation of the semiparametric approach by Escanciano et al. (2016).”
SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES (2014, background): “…and Pakes (1996), Heckman, Ichimura, and Todd (1998), Li and Wooldridge (2002), Levinsohn and Petrin (2003), Blundell and Powell (2004), Linton, Sperlich, and Van Keilegom (2008), Rothe (2009), Escanciano, Jacho-Chávez, and Lewbel (2015) and Caetano, Rothe, and Yildiz (2014), among many others.”