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A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators

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  • Raffaella Giacomini,
  • Dimitris N. Politis
  • Halbert White

Abstract

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

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File URL: http://www.cemmap.ac.uk/wps/cwp111212.pdf
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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP11/12.

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Date of creation: May 2012
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Handle: RePEc:ifs:cemmap:11/12

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  1. Kilian, Lutz & Chang, Pao-Li, 2000. "How accurate are confidence intervals for impulse responses in large VAR models?," Economics Letters, Elsevier, vol. 69(3), pages 299-307, December.
  2. Dimitris N. Politis & Joseph P. Romano & Michael Wolf, 2004. "Inference for Autocorrelations in the Possible Presence of a Unit Root," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 251-263, 03.
  3. Canepa, Alessandra, 2006. "Small sample corrections for linear restrictions on cointegrating vectors: A Monte Carlo comparison," Economics Letters, Elsevier, vol. 91(3), pages 330-336, June.
  4. Davidson, Russell & MacKinnon, James G., 2007. "Improving the reliability of bootstrap tests with the fast double bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3259-3281, April.
  5. Whang, Yoon-Jae, 2000. "Consistent bootstrap tests of parametric regression functions," Journal of Econometrics, Elsevier, vol. 98(1), pages 27-46, September.
  6. Chen, Xiaoheng & Conley, Timothy G., 2001. "A new semiparametric spatial model for panel time series," Journal of Econometrics, Elsevier, vol. 105(1), pages 59-83, November.
  7. Li, Fuchun & Tkacz, Greg, 2006. "A consistent bootstrap test for conditional density functions with time-series data," Journal of Econometrics, Elsevier, vol. 133(2), pages 863-886, August.
  8. Kim, Jae H, 2001. "Bootstrap-after-Bootstrap Prediction Intervals for Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 117-28, January.
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Cited by:
  1. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.
  2. Igor Kheifets & Carlos Velasco, 2013. "New Goodness-of-fit Diagnostics for Conditional Discrete Response Models," Cowles Foundation Discussion Papers 1924, Cowles Foundation for Research in Economics, Yale University.
  3. Moon, Seongman & Velasco, Carlos, 2013. "Tests for m-dependence based on sample splitting methods," Journal of Econometrics, Elsevier, vol. 173(2), pages 143-159.
  4. Igor Kheifets, 2014. "Specification Tests for Nonlinear Dynamic Models," Cowles Foundation Discussion Papers 1937, Cowles Foundation for Research in Economics, Yale University.
  5. Sun, Yixiao & Kim, Min Seong, 2009. "k-step Bootstrap Bias Correction for Fixed Effects Estimators in Nonlinear Panel Models," University of California at San Diego, Economics Working Paper Series qt9gn6n5mr, Department of Economics, UC San Diego.
  6. Su, Jen-Je & Cheung, Adrian (Wai-Kong) & Roca, Eduardo, 2014. "Does Purchasing Power Parity hold? New evidence from wild-bootstrapped nonlinear unit root tests in the presence of heteroskedasticity," Economic Modelling, Elsevier, vol. 36(C), pages 161-171.

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