This dissertation includes two essays. In the first essay, I proposed an alternative estimator for multivariate densities. This estimator can be characterized as a transformation based estimator. The first stage estimates each marginal density separately. In the second stage, the joint density of estimated marginal cumulative distribution functions (CDF) are approximated by the exponential series estimator. The final estimate is then obtained as the product of the marginal densities and the joint density estimated in the second stage. Extensive Monte Carlo studies show the proposed estimator outperforms kernel estimators in joint density and tail distribution estimation. An illustrative example on estimating the conditional copula density between S & P 500 and FTSE 100 given Hangseng and Nikkei 225 is also discussed. In the second essay, I extended the semiparametric model by Chen and Fan [X. Chen, Y. Fan, Estimation of copula-based semiparametric time series models, Journal of Econometrics 130 (2006) 307-335], and studied a class of univariate copula-based nonparametric stationary Markov models in which the copulas and the marginal distributions are estimated nonparametrically. In particular, I focused on the stationary Markov process of order 1 with continuous state space because it has the beta-mixing property for the analysis of weakly dependent processes. The copula density functions for time series models are approximated by the series estimate on sieve spaces. In this study, a finite dimensional linear space spanned by a sequence of power functions is treated as the sieve space where the estimation space of the copula density function is based. This sieve series estimator can be characterized as the exponential series estimator under mild smoothness conditions. By using the beta-mixing properties, I showed that the copula density function approximated by the exponential series estimator for stationary first-order Markov processes has the same convergence rate as the i.i.d. data. The Monte Carlo simulations show that the proposed estimator outperforms the kernel estimator in the conditional density estimation, except for the Frank copula-based Markov model. In addition, the proposed estimator considerably dominates the the kernel estimator when used in the one-step-ahead forecast.

This dissertation provides a thorough overview of the use of nonparametric estimation methods for analyzing data collected by complex sampling plans. Applied econometric analysis is often performed using data collected from large-scale surveys, which use complex sampling plans in order to reduce administrative costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. If one is interested in estimating descriptive statistics, it is highly recommended that one uses an estimator that weights each observation by the inverse of the unit's probability of being included in the sample. If one is interested in estimating causal effects, a weighted estimator should be used if the sampling criterion is correlated with the error term. The sampling criterion is the variable used to design the sampling scheme. If it is correlated with the error term, sampling is said to be endogenous and, if ignored, leads to inconsistent estimation. I consider three distinct probability weighted estimators: i) a nonparametric kernel regression estimator; ii) a conditional probability distribution function estimator; and iii) a nonparametric instrumental variable regression estimator. / Thesis / Doctor of Philosophy (PhD)

The first essay of this dissertation studies the determinants and effects of firms' participation in a voluntary pollution reduction program (VPR) initiated by government regulators. This research presents empirical evidence in support of the "enforcement theory" for VPRs, which predicts that (1) participation is rewarded by relaxed regulatory scrutiny; (2) the anticipation of this reward spurs firms to participate in the program; and (3) the program rewards regulators with reduced pollution. The results also indicate that firms' VPR participation, and pollutant reductions themselves, were prompted by a firm's likelihood of becoming a boycott target and/or being subject to environmental interest group lobbying for tighter standards.In the second essay, a nonparametric regression estimator which can accommodate two empirically relevant data environments is proposed. The first data environment assumes that at least one of the explanatory variables is discrete. In such an environment, a "cell" approach which estimates a separate regression for each discrete cell, has generally been employed. The second data environment assumes that one needs to estimate a set of regression functions that belong to different individuals. In both environments the proposed estimator attempts to reduce estimation error by incorporating extraneous data from the other individuals or "cells" when estimating the regression function for a given individual or "cell". The simulation results for the proposed estimator demonstrate a strong potential in empirical applications.In the third essay, the nonparametric approach proposed in the second essay is used to estimate the parameters of the short-term interest rate diffusion. The nonparametric estimators of the drift of the short rate proposed by Stanton (1997) and Jiang (1998) can produce spurious nonlinearities due to the persistent dependence and limited sampling period of interest rates. The simulations show that the proposed estimator significantly attenuates the spurious nonlinearities of Stanton's nonparametric estimator. An empirical study of the US term structure of interest rates is presented based on the proposed estimator and two other competing models. The results suggest that the estimation of the short rate diffusion parameters using additional data from yields of different maturities has significant economic implications on the valuation interest rate derivatives.

O objetivo dessa dissertação é avaliar o impacto de um programa de treinamento voltado para trabalhadores, utilizando o propensity score matching, mas com dois tipos de abordagem, uma não-paramétrica e a outra semi-paramétrica. Para estimação não paramétrica foi utilizado um método proposto por Li, Racine e Wooldridge (2009) e para estimação semi-paramétrica, o modelo utilizado foi o Generalized Additive Model proposto por Hastie e Tibshirani (1990). Os resultados obtidos indicam que os dois métodos utilizados apresentam estimativas tão boas ou melhores do que quando estimadas paramétricamente. / The goal of this thesis is to evaluate the impact of a job training program using propensity score matching methods with two types of approaches: a nonparametric e another semiparametric. For non-parametric estimation was used a method proposed by Li, Racine and Wooldridge (2009) and for the semiparametric model the Generalized Additive Model proposed by Hastie and Tibshirani (1990). The results indicate that both methods provide estimates as good or better than when parametrically estimated.

O objetivo dessa dissertação é avaliar o impacto de um programa de treinamento voltado para trabalhadores, utilizando o propensity score matching, mas com dois tipos de abordagem, uma não-paramétrica e a outra semi-paramétrica. Para estimação não paramétrica foi utilizado um método proposto por Li, Racine e Wooldridge (2009) e para estimação semi-paramétrica, o modelo utilizado foi o Generalized Additive Model proposto por Hastie e Tibshirani (1990). Os resultados obtidos indicam que os dois métodos utilizados apresentam estimativas tão boas ou melhores do que quando estimadas paramétricamente. / The goal of this thesis is to evaluate the impact of a job training program using propensity score matching methods with two types of approaches: a nonparametric e another semiparametric. For non-parametric estimation was used a method proposed by Li, Racine and Wooldridge (2009) and for the semiparametric model the Generalized Additive Model proposed by Hastie and Tibshirani (1990). The results indicate that both methods provide estimates as good or better than when parametrically estimated.

O objetivo dessa dissertação é avaliar o impacto de um programa de treinamento voltado para trabalhadores, utilizando o propensity score matching, mas com dois tipos de abordagem, uma não-paramétrica e a outra semi-paramétrica. Para estimação não paramétrica foi utilizado um método proposto por Li, Racine e Wooldridge (2009) e para estimação semi-paramétrica, o modelo utilizado foi o Generalized Additive Model proposto por Hastie e Tibshirani (1990). Os resultados obtidos indicam que os dois métodos utilizados apresentam estimativas tão boas ou melhores do que quando estimadas paramétricamente. / The goal of this thesis is to evaluate the impact of a job training program using propensity score matching methods with two types of approaches: a nonparametric e another semiparametric. For non-parametric estimation was used a method proposed by Li, Racine and Wooldridge (2009) and for the semiparametric model the Generalized Additive Model proposed by Hastie and Tibshirani (1990). The results indicate that both methods provide estimates as good or better than when parametrically estimated.