Type of Thesis
In this paper we discuss the derivation, and use a Monte Carlo study to examine the finite sample performances of select estimators put forth in Martins-Filho et al. (2015) for partially linear semiparametric models under nonparametric endogeneity. We find that the selected estimators sufficiently account for the explicit nonparametric endogeneity of the underlying model in finite samples, and conclude that the one step back fitting and unweighted pilot estimators are more efficient than their counterparts in estimating m(x) and β respectively. Our findings support the assertion that both estimators for m(x) are oracle efficient.
Anderson, David, "Finite Sample Performance of Semiparametric Estimation Methods for Partially Linear Models Under Nonparametric Endogeneity" (2015). Undergraduate Honors Theses. 900.