Conditional density estimation: an application to the Ecuadorian manufacturing sector

This note applies conditional density estimation as a visual method to present results. The proposed method is illustrated by application to a firm-level manufacturing data set from Ecuador in 2002.Density Estimation

Uniform in Bandwidth Consistency of Smooth Varying Coefficient Estimators

We prove the strong consistency, uniformly in the bandwidth, of the smooth varying coefficient conditional least squares estimator. Our results justify data-driven choices of bandwidths, such as Silverman's rule-of thumb, or standard cross-validation, that are usually implemented by most practitioners.Kernel estimators, empirical processes, varying coefficient models

Identification, estimation and efficiency of nonparametric and semiparametric models in microeconometrics.

The focal point of this thesis is on identification and estimation of nonparametric models, as well as the efficiency and higher order properties of a class of semiparametric estimators in Microeconometrics. We present a new identification result for a particular nonparametric model that nests many popular parametric/nonparametric Econometric models as special cases. Estimators are proposed and their asymptotic properties derived; in particular, they are shown to be consistent and asymptotically pointwise normally distributed. We implement these estimators for the nonparametric estimation and testing of production functions in 4 industries within the Chinese economy in the years 1995 and 2001. The statistical properties of an entire family of semiparametric estimators for Limited Dependent Variables models are also analyzed. The derived theoretical results have direct applicability to a wide range of estimation problems. In particular, we derive the semiparametric efficiency bounds and show that some of the already-proposed estimators achieve these bounds. A connection with the Programme Evaluation literature is established as well. Finally, we derive an asymptotic approximation to the Mean Square Error of this class of semiparametric estimators to aid the choice of smoothing parameter. It is demonstrated that this choice can be made on the basis of bias alone. Possible extensions in this framework are also discussed

Identification and estimation of semiparametric two-step models

. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector ß0 and unknown function F0 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.Escanciano’s research was funded by the Spanish Plan Nacional de I+D+i, reference number ECO2012-33053

Functionals of order statistics and their multivariate concomitants with application to semiparametric estimation by nearest neighbours

This paper studies the limiting behavior of general functionals of order statistics and their multivariate concomitants for weakly dependent data. The asymptotic analysis is performed under a conditional moment-based notion of dependence for vector-valued time series. It is argued, through analysis of various examples, that the dependence conditions of this type can be effectively implied by other dependence formations recently proposed in time-series analysis, thus it may cover many existing linear and nonlinear processes. The utility of this result is then illustrated in deriving the asymptotic properties of a semiparametric estimator that uses the k-Nearest Neighbour estimator of the inverse of a multivariate unknown density. This estimator is then used to calculate consumer surpluses for electricity demand in Ontario for the period 1971 to 1994. A Monte Carlo experiment also assesses the effi- cacy of the derived limiting behavior in finite samples for both these general functionals and the proposed estimator

Semiparametric estimation of moment condition models with weakly dependent data

This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized estimating equations models with weakly dependent data. The paper proposes new estimation methods based on smoothed two-step versions of the generalised method of moments and generalised empirical likelihood methods. An important aspect of the paper is that it allows the first-step estimation to have an effect on the asymptotic variances of the second-step estimators and explicitly characterises this effect for the empirically relevant case of the so-called generated regressors. The results of the paper are illustrated with a partially linear model that has not been previously considered in the literature. The proofs of the results utilise a new uniform strong law of large numbers and a new central limit theorem for U-statistics with varying kernels that are of independent interest

Functionals of order statistics and their multivariate concomitants with application to semiparametric estimation by nearest neighbours

This paper studies the limiting behavior of general functionals of order statistics and their multivariate concomitants for weakly dependent data. The asymptotic analysis is performed under a conditional moment-based notion of dependence for vector-valued time series. It is argued, through analysis of various examples, that the dependence conditions of this type can be effectively implied by other dependence formations recently proposed in time-series analysis, thus it may cover many existing linear and nonlinear processes. The utility of this result is then illustrated in deriving the asymptotic properties of a semiparametric estimator that uses the k-Nearest Neighbour estimator of the inverse of a multivariate unknown density. This estimator is then used to calculate consumer surpluses for electricity demand in Ontario for the period 1971 to 1994. A Monte Carlo experiment also assesses the effi- cacy of the derived limiting behavior in finite samples for both these general functionals and the proposed estimator

Nonlinear difference-indifference treatment effect estimation: A distributional analysis

This chapter uses the nonlinear difference-in-difference (NL-DID) methodology developed by Athey and Imbens (2006) to estimate the effects of a treatment program on the entire distribution of an outcome variable. The NL-DID estimates the entire counterfactual distribution of an outcome variable that would have occurred in the absence of treatment. This chapter extends the Monte Carlo results in Athey and Imbens's (2006) to assess the efficacy of the NL-DID estimators in finite samples. Furthermore, the NL-DID methodology recovers the entire outcome distribution in the absence of treatment. Further, we consider the empirical size and power of tests statistics for equality of mean, medians, and complete distributions as suggested by Abadie (2002). The results show that the NL-DID estimator can effectively be used to recover the average treatment effect, as well as the entire distribution of the treatment effects when there is no selection during the treatment period in finite samples. Copyrigh