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Approximate Bias Correction in Econometrics

  • James G. MacKinnon
  • Anthony A. Smith, Jr.

This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several evaluations of the bias function will be required to do this. Unfortunately, reducing bias may increase the variance, or even the mean squared error, of an estimator. Whether or not it does so depends on the shape of the bias functions. The techniques of the paper are illustrated by applying them to two problems: estimating the autoregressive parameter in an AR(1) model with a constant term, and estimation of a logit model.

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Paper provided by Carnegie Mellon University, Tepper School of Business in its series GSIA Working Papers with number 1997-36.

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Handle: RePEc:cmu:gsiawp:91
Contact details of provider: Postal: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890
Web page: http://www.tepper.cmu.edu/

Order Information: Web: http://student-3k.tepper.cmu.edu/gsiadoc/GSIA_WP.asp

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  1. Gourieroux, C. & Monfort, A. & Renault, E., 1992. "Indirect Inference," Papers 92.279, Toulouse - GREMAQ.
  2. MacKinnon, James G. & Smith Jr., Anthony A., 1998. "Approximate bias correction in econometrics," Journal of Econometrics, Elsevier, vol. 85(2), pages 205-230, August.
  3. Chesher, Andrew, 1995. "A Mirror Image Invariance for M-Estimators," Econometrica, Econometric Society, vol. 63(1), pages 207-11, January.
  4. Chesher, Andrew & Peters, Simon, 1994. "Symmetry, Regression Design, and Sampling Distributions," Econometric Theory, Cambridge University Press, vol. 10(01), pages 116-129, March.
  5. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S63-84, Suppl. De.
  6. Phillips, Peter C. B., 1988. "The ET Interview: Professor James Durbin," Econometric Theory, Cambridge University Press, vol. 4(01), pages 125-157, April.
  7. repec:cup:etheor:v:10:y:1994:i:1:p:116-29 is not listed on IDEAS
  8. Kiviet, Jan F. & Phillips, Garry D.A., 1993. "Alternative Bias Approximations in Regressions with a Lagged-Dependent Variable," Econometric Theory, Cambridge University Press, vol. 9(01), pages 62-80, January.
  9. Sawa, Takamitsu, 1978. "The exact moments of the least squares estimator for the autoregressive model," Journal of Econometrics, Elsevier, vol. 8(2), pages 159-172, October.
  10. Kiviet, Jan F. & Phillips, Garry D. A., 1994. "Bias assessment and reduction in linear error-correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 215-243, July.
  11. 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.
  12. Russell Davidson & James G. Mackinnon, 1990. "Regression-Based Methods for Using Control and Antithetic Variates in Monte Carlo Experiments," Working Papers 781, Queen's University, Department of Economics.
  13. Orcutt, Guy H & Winokur, Herbert S, Jr, 1969. "First Order Autoregression: Inference, Estimation, and Prediction," Econometrica, Econometric Society, vol. 37(1), pages 1-14, January.
  14. repec:cup:etheor:v:9:y:1993:i:1:p:62-80 is not listed on IDEAS
  15. Andrews, Donald W K, 1993. "Exactly Median-Unbiased Estimation of First Order Autoregressive/Unit Root Models," Econometrica, Econometric Society, vol. 61(1), pages 139-65, January.
  16. Tony Smith & Fallaw Sowell & Stanley Zin, . "Fractional integration with Drift: Estimation in Small Samples," GSIA Working Papers 22, Carnegie Mellon University, Tepper School of Business.
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