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Assessing the effect of high performance computing capabilities on academic research output

Author

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  • Amy Apon

    ()

  • Linh Ngo

    ()

  • Michael Payne

    ()

  • Paul Wilson

    ()

Abstract

This paper uses nonparametric methods and some new results on hypothesis testing with nonparametric efficiency estimators and applies these to analyze the effect of locally available high performance computing (HPC) resources on universities’ efficiency in producing research and other outputs. We find that locally available HPC resources enhance the technical efficiency of research output in Chemistry, Civil Engineering, Physics, and History, but not in Computer Science, Economics, nor English; we find mixed results for Biology. Our research results provide a critical first step in a quantitative economic model for investments in HPC. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Amy Apon & Linh Ngo & Michael Payne & Paul Wilson, 2015. "Assessing the effect of high performance computing capabilities on academic research output," Empirical Economics, Springer, vol. 48(1), pages 283-312, February.
  • Handle: RePEc:spr:empeco:v:48:y:2015:i:1:p:283-312
    DOI: 10.1007/s00181-014-0833-7
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    File URL: http://hdl.handle.net/10.1007/s00181-014-0833-7
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    References listed on IDEAS

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    1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    3. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    4. Simar, Léopold & Wilson, Paul W., 2013. "Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives," Foundations and Trends(R) in Econometrics, now publishers, vol. 5(3–4), pages 183-337, June.
    5. Léopold Simar & Paul Wilson, 1999. "Some Problems with the Ferrier/Hirschberg Bootstrap Idea," Journal of Productivity Analysis, Springer, vol. 11(1), pages 67-80, February.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Alois Kneip & Léopold Simar & Paul Wilson, 2011. "A Computationally Efficient, Consistent Bootstrap for Inference with Non-parametric DEA Estimators," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 483-515, November.
    8. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    9. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    10. Furman, Jeffrey L. & Porter, Michael E. & Stern, Scott, 2002. "The determinants of national innovative capacity," Research Policy, Elsevier, vol. 31(6), pages 899-933, August.
    11. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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    1. repec:eee:ejores:v:267:y:2018:i:1:p:349-367 is not listed on IDEAS

    More about this item

    Keywords

    Efficiency; Frontier; Nonparametric; Inference; C12; C14; C44; H52;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • H52 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Education

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