The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Bootstrap

  • Joel Horowitz
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2776

Abstract

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data. It is often much more accurate in finite samples than ordinary asymptotic approximations are. This is important in applied research, because the familiar asymptotic normal and chi-square approximations can be very inaccurate. When this happens, the difference between the true and nominal coverage probability of a confidence interval or rejection probability of a test can be very large, and inference can be highly misleading. The bootstrap often greatly reduces errors in coverage and rejection probabilities, thereby making reliable inference possible.

Keywords

Asymptotic distribution Asymptotic refinements Bias reduction Bootstrap Conditional Kolmogorov test statistic Edgeworth approximations Maximum score estimator Monte Carlo simulation Probability Probit models Statistical inference Statistics and economics Subsampling Tobit model 

JEL Classifications

C15 
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Notes

Acknowledgments

I thank Federico Bugni for helpful comments. The preparation of this article was supported in part by NSF Grant SES-0352675.

Bibliography

  1. Abowd, J.M., and D. Card. 1989. On the covariance of earnings and hours changes. Econometrica 57: 411–445.CrossRefGoogle Scholar
  2. Andrews, D.W.K. 1997. A conditional Kolmogorov test. Econometrica 65: 1097–1128.CrossRefGoogle Scholar
  3. Beran, R., and G.R. Ducharme. 1991. Asymptotic theory for bootstrap methods in statistics. Montréal: Les Publications CRM, Centre de Recherches Mathematiques, Université de Montréal.Google Scholar
  4. Chesher, A. 1983. The information matrix test. Economics Letters 13: 45–48.CrossRefGoogle Scholar
  5. Davidson, R., and J.G. MacKinnon. 1999. The size distortion of bootstrap tests. Econometric Theory 15: 361–376.CrossRefGoogle Scholar
  6. Davison, A.C., and D.V. Hinkley. 1997. Bootstrap methods and their application. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  7. Efron, B. 1979. Bootstrap methods: Another look at the jackknife. Annals of Statistics 7: 1–26.CrossRefGoogle Scholar
  8. Efron, B., and R.J. Tibshirani. 1993. An introduction to the bootstrap. New York: Chapman & Hall.CrossRefGoogle Scholar
  9. Hall, P. 1992. The bootstrap and edgeworth expansion. New York: Springer.CrossRefGoogle Scholar
  10. Hall, P. 1994. Methodology and theory for the bootstrap. In Handbook of econometrics, ed. R.F. Engle and D.F. McFadden, Vol. 4. Amsterdam: North-Holland.Google Scholar
  11. Horowitz, J.L. 1994. Bootstrap-based critical values for the information matrix test. Journal of Econometrics 61: 395–411.CrossRefGoogle Scholar
  12. Horowitz, J.L. 1997. Bootstrap methods in econometrics: Theory and numerical performance. In Advances in economics and econometrics: Theory and applications, seventh world congress, ed. D.M. Kreps and K.F. Wallis, Vol. 3. Cambridge: Cambridge University Press.Google Scholar
  13. Horowitz, J.L. 1998. Bootstrap methods for covariance structures. Journal of Human Resources 33: 39–61.CrossRefGoogle Scholar
  14. Horowitz, J.L. 2001. The bootstrap in econometrics. In Handbook of econometrics, ed. J.J. Heckman and E.E. Leamer, Vol. 5. Amsterdam: North-Holland.Google Scholar
  15. Horowitz, J.L. 2003. The bootstrap in econometrics. Statistical Science 18: 211–218.CrossRefGoogle Scholar
  16. Horowitz, J.L., and C.F. Manski. 2000. Nonparametric analysis of randomized experiments with missing covariate and outcome data. Journal of the American Statistical Association 95: 77–84.CrossRefGoogle Scholar
  17. Lahiri, S.N. 2003. Resampling methods for dependent data. New York: Springer.CrossRefGoogle Scholar
  18. Lancaster, T. 1984. The covariance matrix of the information matrix test. Econometrica 52: 1051–1053.CrossRefGoogle Scholar
  19. Maddala, G.S., and J. Jeong. 1993. A perspective on application of bootstrap methods in econometrics. In Handbook of statistics, ed. G.S. Maddala, C.R. Rao, and H.D. Vinod, Vol. 11. Amsterdam: North-Holland.Google Scholar
  20. Mammen, E. 1992. When does bootstrap work? Asymptotic results and simulations. New York: Springer.CrossRefGoogle Scholar
  21. Manski, C.F. 1975. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3: 205–228.CrossRefGoogle Scholar
  22. Manski, C.F. 1985. Semiparametric analysis of discrete response: Asymptotic properties of the maximum score estimator. Journal of Econometrics 27: 313–334.CrossRefGoogle Scholar
  23. Politis, D.N., J.P. Romano, and M. Wolf. 1999. Subsampling. New York: Springer.CrossRefGoogle Scholar
  24. Shao, U., and D. Tu. 1995. The jackknife and bootstrap. New York: Springer.CrossRefGoogle Scholar
  25. Vinod, H.D. 1993. Bootstrap methods: Applications in econometrics. In Handbook of statistics, ed. G.S. Maddala, C.R. Rao, and H.D. Vinod, Vol. 11. Amsterdam: North-Holland.Google Scholar
  26. White, H. 1982. Maximum likelihood estimation of misspecified models. Econometrica 50: 1–26.CrossRefGoogle Scholar

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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Joel Horowitz
    • 1
  1. 1.