Parametric Conditional Monte Carlo Density Estimation

Yin Liao (yin.liao@mq.edu.au) and John Stachurski (john.stachurski@anu.edu.au)

ANU Working Papers in Economics and Econometrics from Australian National University, College of Business and Economics, School of Economics

Abstract: In applied density estimation problems, one often has data not only on the target variable, but also on a collection of covariates. In this paper, we study a density estimator that incorporates this additional information by combining parametric estimation and conditional Monte Carlo. We prove an approximate functional asymptotic normality result that illustrates convergence rates and the asymptotic variance of the estimator. Through simulation, we illustrate the strength of its finite sample properties in a number of standard econometric and financial applications.

Pages: 26 Pages
Date: 2011-10
New Economics Papers: this item is included in nep-cis and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:acb:cbeeco:2011-562

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