Semiparametric Maximum Likelihood Sieve Estimator for Correction of Endogenous Truncation Bias

43 Pages Posted: 11 Dec 2018

See all articles by Nir Billfeld

Nir Billfeld

University of Haifa

Moshe Kim

University of Haifa - Department of Economics

Date Written: November 18, 2018

Abstract

Semiparametric correction for a sample selection bias in the presence of endogenous truncation is known to be much more difficult in the case of a binary selection variable than in the case of a continuous selection variable. This paper proposes a simple bandwidth-free semiparametric methodology to correct for a self-selection bias in a truncated sample, without any prior knowledge of the marginal density functions of the selection model’s random disturbances. Each of the unknown marginal density functions is estimated using Sieve estimator, utilizing Hermite polynomials as basis functions. The aforementioned procedure is appropriate for both binary and continuous selection variables cases under the covariate shift assumption. We consider a double hurdle model, which is a combination of two selection rules. The first is propagated by a truncation in the dependent variable of the substantive equation. The second is propagated by endogenous self-selection. The suggested correction procedure produces estimates that are of high accuracy and consistent based on Monte Carlo simulations. The random disturbances are not restricted to being symmetric and their marginal distribution functions are unknown. Thus, in the data generation process we verify the applicability of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed. These disturbances are constructed as realizations of non-symmetric distribution functions.

Keywords: Selectivity Bias Correction, Hermite Polynomials, Covariate Shift

JEL Classification: C01, C14, C26

Suggested Citation

Billfeld, Nir and Kim, Moshe, Semiparametric Maximum Likelihood Sieve Estimator for Correction of Endogenous Truncation Bias (November 18, 2018). Available at SSRN: https://ssrn.com/abstract=3286553 or http://dx.doi.org/10.2139/ssrn.3286553

Nir Billfeld

University of Haifa ( email )

Mount Carmel
Haifa, 31905
Israel

Moshe Kim (Contact Author)

University of Haifa - Department of Economics ( email )

Haifa 31905
Israel
(972) 4 8240115 (Phone)
(972)4-8240059 (Fax)

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