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Regression-based methods for using control variates in Monte Carlo experiments

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  • Davidson, Russell
  • MacKinnon, James G.

Abstract

Methods based on linear regression provide an easy way to use the information in control variates to improve the efficiency with which certain features of the distributions of estimators and test statistics are estimated in Monte Carlo experiments. We propose a new technique that allows these methods to be used when the quantities of interest are quantiles. We also propose new ways to obtain approximately optimal control variates in many cases of interest. These methods seem to work well in practice, and can greatly reduce the number of replications required to obtain a given level of accuracy.
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Suggested Citation

  • Davidson, Russell & MacKinnon, James G., 1992. "Regression-based methods for using control variates in Monte Carlo experiments," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 203-222.
  • Handle: RePEc:eee:econom:v:54:y:1992:i:1-3:p:203-222
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    1. D. F. Hendry & P. K. Trivedi, 1972. "Maximum Likelihood Estimation of Difference Equations with Moving Average Errors: A Simulation Study," Review of Economic Studies, Oxford University Press, vol. 39(2), pages 117-145.
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    3. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    4. Tauchen, George, 1985. "Diagnostic testing and evaluation of maximum likelihood models," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 415-443.
    5. Nankervis, J C & Savin, N E, 1988. "The Student's t Approximation in a Stationary First Order Autoregressive Model," Econometrica, Econometric Society, vol. 56(1), pages 119-145, January.
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    Cited by:

    1. MacKinnon, James G. & Smith Jr., Anthony A., 1998. "Approximate bias correction in econometrics," Journal of Econometrics, Elsevier, vol. 85(2), pages 205-230, August.
    2. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(03), pages 361-376, June.
    3. Russell Davidson & James G. MacKinnon, 1996. "The Size and Power of Bootstrap Tests," Working Papers 932, Queen's University, Department of Economics.
    4. Zweimuller, J & Winter-Ebmer, R, 1994. "Gender Wage Differentials in Private and Public Sector Jobs," Journal of Population Economics, Springer;European Society for Population Economics, vol. 7(3), pages 271-285, July.
    5. Racine, Jeffrey S. & MacKinnon, James G., 2007. "Inference via kernel smoothing of bootstrap P values," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5949-5957, August.
    6. Arnold de Silva, 1999. "Wage Discrimination Against Natives," Canadian Public Policy, University of Toronto Press, vol. 25(1), pages 65-85, March.
    7. Sadraoui, Tarek & Ben Zina, Naceur, 2007. "Coopération en R&D et croissance économique : Une analyse par les données de panel dynamique
      [R&D Cooperation and economic growth: A dynamic panel data analysis]
      ," MPRA Paper 3415, University Library of Munich, Germany.
    8. Lee C. Adkins, 2011. "Monte Carlo Experiments Using gretl: A Primer," Economics Working Paper Series 1103, Oklahoma State University, Department of Economics and Legal Studies in Business.
    9. Timothy C. Hesterberg & Barry L. Nelson, 1998. "Control Variates for Probability and Quantile Estimation," Management Science, INFORMS, vol. 44(9), pages 1295-1312, September.

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