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## Bootstrap Methods

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This RePEc Biblio topic is edited by Pierangelo De Pace. It was first published on 2013-01-16 10:55:29 and last updated on 2016-03-11 22:40:03.

#### Introduction by the editor

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling oneâ€™s data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under mild regularity conditions, the bootstrap yields an approximation to the distribution of an estimator or test statistic that is at least as accurate as the approximation obtained from first-order asymptotic theory. Thus, the bootstrap provides a way to substitute computation for mathematical analysis if calculating the asymptotic distribution of an estimator or statistic is difficult. (Horowitz, 2001)

Seminal article. Efron, B. (1979). Bootstrap methods: another look at the jackknife, Annals of Statistics, 7, 1-26.

#### Most relevant link for this topic

http://en.wikipedia.org/wiki/Bootstrapping_(statistics)

#### Most relevant JEL codes

• C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

#### Most relevant research

1. Horowitz, Joel L., 2001. "The Bootstrap," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228 Elsevier.
2. Hall, Peter, 1986. "Methodology and theory for the bootstrap," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 39, pages 2341-2381 Elsevier.
3. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
4. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.
5. Donald W.K. Andrews & Moshe Buchinsky, 1997. "On the Number of Bootstrap Repetitions for Bootstrap Standard Errors, Confidence Intervals, and Tests," Cowles Foundation Discussion Papers 1141R, Cowles Foundation for Research in Economics, Yale University.
6. Andrews, Donald W.K. & Buchinsky, Moshe, 2002. "ON THE NUMBER OF BOOTSTRAP REPETITIONS FOR BCa CONFIDENCE INTERVALS," Econometric Theory, Cambridge University Press, vol. 18(04), pages 962-984, August.
7. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, EconWPA, revised 05 Mar 1996.
8. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages S2-S18, 09.
9. Babu, G. J. & Bose, A., 1988. "Bootstrap confidence intervals," Statistics & Probability Letters, Elsevier, vol. 7(2), pages 151-160, September.
10. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
11. Lahiri, Soumendra Nath, 1991. "Second order optimality of stationary bootstrap," Statistics & Probability Letters, Elsevier, vol. 11(4), pages 335-341, April.
12. Chung, Han-Yeong & Lee, Kee-Won & Koo, Ja-Yong, 1996. "A note on bootstrap model selection criterion," Statistics & Probability Letters, Elsevier, vol. 26(1), pages 35-41, January.
13. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.