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The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests

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  • David Brownstone
  • Robert Valletta

Abstract

The bootstrap and multiple imputations are two techniques that can enhance the accuracy of estimated confidence bands and critical values. Although they are computationally intensive, relying on repeated sampling from empirical data sets and associated estimates, modern computing power enables their application in a wide and growing number of econometric settings. We provide an intuitive overview of how to apply these techniques, referring to existing theoretical literature and various applied examples to illustrate both their possibilities and their pitfalls.

Suggested Citation

  • David Brownstone & Robert Valletta, 2001. "The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 129-141, Fall.
  • Handle: RePEc:aea:jecper:v:15:y:2001:i:4:p:129-141
    Note: DOI: 10.1257/jep.15.4.129
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    References listed on IDEAS

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    1. Brownstone, David & Golob, Thomas F. & Kazimi, Camilla, 1991. "Modeling non-ignorable attrition and measurement error in panel surveys: an application to travel demand modeling," University of California Transportation Center, Working Papers qt7sh4d67b, University of California Transportation Center.
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    5. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    6. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    7. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    8. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, University Library of Munich, Germany, revised 05 Mar 1996.
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    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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