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A WARP-SPEED METHOD FOR CONDUCTING MONTE CARLO EXPERIMENTS INVOLVING BOOTSTRAP ESTIMATORS

Published online by Cambridge University Press:  16 January 2013

Raffaella Giacomini ,
Dimitris N. Politis  and
Halbert White
Raffaella Giacomini*
Affiliation:
University College London/CeMMAP
Dimitris N. Politis
Affiliation:
University of California, San Diego
Halbert White
Affiliation:
University of California, San Diego
*
*Address correspondence to Raffaella Giacomini, University College London, Department of Economics, Gower Street, London WC1E6BT, UK; e-mail: r.giacomini@ucl.ac.uk.
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Abstract

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We analyze fast procedures for conducting Monte Carlo experiments involving bootstrap estimators, providing formal results establishing the properties of these methods under general conditions.

Information

Type
Articles
Information
Econometric Theory , Volume 29 , Issue 3 , June 2013 , pp. 567 - 589
Copyright
Copyright © Cambridge University Press 2013 

Footnotes

This paper is dedicated to the memory of Halbert White, inspiring mentor, friend and colleague, master econometrician, and jazz musician. He was a true scholar and an exceptional human being. Raffaella Giacomini gratefully acknowledges financial support from the Economic and Social Research Council through the ESRC Centre for Microdata Methods and Practice grant RES-589-28-0001. Dimitris Politis gratefully acknowledges partial support from NSF Grant DMS-10-07513.

References

REFERENCES

Canepa, A. (2006) Small sample corrections for linear restrictions on cointegrating vectors: A monte Carlo comparison. Economics Letters 91, 330–336.CrossRefGoogle Scholar
Chen, X. & Conley, T.G. (2001) A new semiparametric spatial model for panel time series. Journal of Econometrics 105, 59–83.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J. (2000) Improving the Reliability of Bootstrap Tests. Queen’s University Working paper no. 995.Google Scholar
Davidson, R. & MacKinnon, J. (2002) Fast double bootstrap tests of nonnested linear regression models. Econometric Reviews 21, 417–427.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J. (2007) Improving the reliability of bootstrap tests with the fast double bootstrap. Computational Statistics and Data Analysis 51, 3259–3281.CrossRefGoogle Scholar
Efron, B. (1979) Bootstrap methods: Another look at the jackknife. Annals of Statistics 7, 1–26.CrossRefGoogle Scholar
Efron, B. (2000) The bootstrap and modern statistics. Journal of the American Statistical Association 95, 1293–1296.CrossRefGoogle Scholar
Escanciano, J.C. & Velasco, C. (2006) Generalized spectral tests for the martingale difference hypothesis. Journal of Econometrics 134, 151–185.CrossRefGoogle Scholar
Kilian, L. & Chang, P.L. (2000) How accurate are confidence intervals for impulse responses in large VAR models? Economics Letters 69, 299–307.CrossRefGoogle Scholar
Kim, J.H. (2001) Bootstrap-after-bootstrap prediction intervals for autoregressive models. Journal of Business and Economic Statistics 19, 117–128.CrossRefGoogle Scholar
Künsch, H.R. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17, 1217–1241.CrossRefGoogle Scholar
Lehmann, E.L. & Romano, J.P. (2005) Testing Statistical Hypotheses, 3rd ed., Springer Verlag.Google Scholar
Li, F. & Tkacz, G. (2006) A consistent bootstrap test for conditional density functions with time-series data. Journal of Econometrics 133, 863–886.CrossRefGoogle Scholar
Loh, W.Y. (1988) Discussion of “Theoretical comparison of bootstrap confidence intervals,” by Hall, P.. Annals of Statistics 16, 972–976.Google Scholar
Loh, W.Y. (1991) Bootstrap calibration for confidence interval construction and selection. Statistica Sinica 1, 479–495.Google Scholar
Politis, D.N. & Romano, J.P. (1994) The stationary bootstrap. Journal of the American Statistical Association 89, 1303–1313.CrossRefGoogle Scholar
Politis, D.N., Romano, J.P., & Wolf, M. (1999) Subsampling. Springer Verlag.CrossRefGoogle Scholar
Politis, D.N., Romano, J.P., & Wolf, M. (2004) Inference for autocorrelations in the possible presence of a unit root. Journal of Time Series Analysis 25, 251–263.CrossRefGoogle Scholar
Politis, D.N. & White, H. (2004) Automatic block-length selection for the dependent bootstrap. Econometric Reviews 23, 53–70. (With correction: A. Patton, D.N. Politis, & H. White (2009) Correction to “Automatic block-length selection for the dependent bootstrap” by D. Politis and H. White. Econometric Reviews 28, 372–375).CrossRefGoogle Scholar
Shao, J. & Tu, D. (1995) The Jackknife and the Bootstrap. Springer Verlag.Google Scholar
Whang, Y.J. (2000) Consistent bootstrap tests of parametric regression functions. Journal of Econometrics 98, 27–46.CrossRefGoogle Scholar
White, H. (1998) A reality check for data snooping. QRDA Technical Report 8/98.Google Scholar