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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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  • 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. P. Rothery, 1982. "The Use of Control Variates in Monte Carlo Estimation of Power," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 125-129, June.
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    6. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976, Elsevier.
    7. 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(3), pages 361-376, June.
    3. James G. MacKinnon & Russell Davidson, 1996. "The Size And Power Of Bootstrap Tests," Working Paper 932, Economics Department, Queen's University.
    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. Arnold de Silva, 1999. "Wage Discrimination Against Natives," Canadian Public Policy, University of Toronto Press, vol. 25(1), pages 65-85, March.
    6. 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.
    7. 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.
    8. 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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