Nonparametric Econometric Methods

Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.

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This Volume of "Advances in Econometrics" contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. The theme of the conference was 'Nonparametric Econometric Methods', and the papers selected for inclusion in this Volume span a range of nonparametric techniques including kernel smoothing, empirical copulas, series estimators, and smoothing splines along with a variety of semiparametric methods. The papers in this Volume cover topics of interest to those who wish to familiarize themselves with current nonparametric methodology. Many papers also identify areas deserving of future attention. There exist survey papers devoted to recent developments in nonparametric nance, constrained nonparametric regression, miparametric/nonparametric environmental econometrics and nonparametric models with non-stationary data. There exist theoretical papers dealing with novel approaches for partial identification of the distribution of treatment effects, xed effects semiparametric panel data models, functional coefficient models with time series data, exponential series estimators of empirical copulas, estimation of multivariate CDFs and bias-reduction methods for density estimation. There also exist a number of applications that analyze returns to education, the evolution of income and life expectancy, the role of governance in growth, farm production, city size and unemployment rates, derivative pricing, and environmental pollution and economic growth. In short, this Volume contains a range of theoretical developments, surveys, and applications that would be of interest to those who wish to keep abreast of some of the most important current developments in the field of nonparametric estimation.