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Parametric Conditional Monte Carlo Density Estimation

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  • Yin Liao

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  • John Stachurski

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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.

Suggested Citation

  • Yin Liao & John Stachurski, 2011. "Parametric Conditional Monte Carlo Density Estimation," ANU Working Papers in Economics and Econometrics 2011-562, Australian National University, College of Business and Economics, School of Economics.
  • Handle: RePEc:acb:cbeeco:2011-562
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp562.pdf
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    References listed on IDEAS

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    1. Zhao, Zhibiao, 2010. "Density estimation for nonlinear parametric models with conditional heteroscedasticity," Journal of Econometrics, Elsevier, vol. 155(1), pages 71-82, March.
    2. Siem Jan Koopman & Eugenie Hol Uspensky, 2002. "The stochastic volatility in mean model: empirical evidence from international stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 667-689.
    3. Daniel R. Smith & Allan Layton, 2007. "Comparing Probability Forecasts in Markov Regime Switching Business Cycle Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(1), pages 79-98.
    4. R. Anton Braun & Huiyu Li & John Stachurski, 2011. "Generalized Look-Ahead Methods for Computing Stationary Densities," ANU Working Papers in Economics and Econometrics 2011-558, Australian National University, College of Business and Economics, School of Economics.
    5. Aït-Sahalia, Yacine & Fan, Jianqing & Peng, Heng, 2009. "Nonparametric Transition-Based Tests for Jump Diffusions," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1102-1116.
    6. He, Zhongzhi (Lawrence) & Huh, Sahn-Wook & Lee, Bong-Soo, 2010. "Dynamic Factors and Asset Pricing," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(03), pages 707-737, June.
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