Effective Nonparametric Estimation in the Case of Severely Discretized Data

Duke Economics Working Paper No. 00-03

33 Pages Posted: 22 Nov 2000

See all articles by Mark Coppejans

Mark Coppejans

BlackRock, Inc; Barclays Global Investors

Date Written: 2000

Abstract

Almost all economic data sets are discretized or rounded to some extent. This paper proposes a regression and a density estimator that work especially well when the data is very discrete. The estimators are a weighted average of the data, and the weights are composed of cubic B-splines. Unlike most nonparametric settings, where it is assumed that the observed data comes from a continuum of possibilities, we base our work on the assumption that the discreteness becomes finer as the sample size increases. Rates of convergence and asymptotic distributional results are derived under this condition.

Key Words: Discretization, Nonparametric Density Estimators, Nonparametric Regression Estimators, B-splines, Kernels

JEL Classification: C14

Suggested Citation

Coppejans, Mark T., Effective Nonparametric Estimation in the Case of Severely Discretized Data (2000). Duke Economics Working Paper No. 00-03, Available at SSRN: https://ssrn.com/abstract=235741 or http://dx.doi.org/10.2139/ssrn.235741

Mark T. Coppejans (Contact Author)

BlackRock, Inc ( email )

San Francisco, CA
United States

Barclays Global Investors ( email )

45 Fremont Street
San Francisco, CA 94105
United States

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