We estimate income elasticities for a variety of macro- and micro-nutrients using a sample of poor rural households in Mexico. The nutrient-income elasticity is estimated using a linear regression model controlling both for the clustered nature of our data and for the bias due to measurement...

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of...

This paper studies the drivers of the daily dynamics of the nominal dinar-euro exchange rate from September 2006 to June 2010. Using a novel semiparametric approach we are able to incorporate the evidence of nonlinearities under very weak assumptions on the underlying data generating process. We...

We consider the semiparametric regression X t +(Z) where and (r and function, and where the variables (X, Z) are endogeneous. We propose necessary and sufficient conditions for the identification of the parameters in the presence of instrumental variables. We also focus on the estimation of . An...

Model selection in nonparametric and semiparametric regression is of both theoretical and practical interest. Gao and Tong (2004) proposed a semiparametric leave–more–out cross–validation selection procedure for the choice of both the parametric and nonparametric regressors in a nonlinear...

It is well established that local linear method dominates the conventional local constant method in estimating nonparametric regression models by kernel method. In this paper we consider the problem of estimating semiparametric econometric models by local linear method. We provide a simple proof...

This study conducts Monte Carlo simulation exercises to determine the finite sample properties of the Fan-Li test [1996] when the interest focuses on uncovering the impact of omitted variables on the semiparametric partially linear model (PLM). It essentially subjects the said test to various...

We consider the problem of estimating a varying coefficient regression model when regressors include a time trend. We show that the commonly used local constant kernel estimation method leads to an inconsistent estimation result, while a local polynomial estimator yields a consistent estimation...

We consider the identification of and inference on a partially linear model, when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using...

Independent component analysis (ICA) is a modern factor analysis tool developed in the last two decades. Given p-dimensional data, we search for that linear combination of data which creates (almost) independent components. Here copulae are used to model the p-dimensional data and then...