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Forecasting the Malmquist productivity index

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  • Daskovska, Alexandra
  • Simar, Leopold
  • Van Bellegem, Sebastien

Abstract

The Malmquist Productivity Index (MPI) suggests a convenient way of measuring the productivity change of a given unit between two consequent time periods. Until now, only a static approach for analyzing the MPI was available in the literature. However, this hides a potentially valuable information given by the evolution of productivity over time. In this paper, we introduce a dynamic procedure for forecasting the MPI. We compare several approaches and give credit to a method based on the assumption of circularity. Because the MPI is not circular, we present a new decomposition of the MPI, in which the time-varying indices are circular. Based on that decomposition, a new working dynamic forecasting procedure is proposed and illustrated. To construct prediction intervals of the MPI, we extend the bootstrap method in order to take into account potential serial correlation in the data. We illustrate all the new techniques described above by forecasting the productivityt index of 17 OCDE countries, constructed from their GDP, labor and capital stock.
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Suggested Citation

  • Daskovska, Alexandra & Simar, Leopold & Van Bellegem, Sebastien, 2010. "Forecasting the Malmquist productivity index," LIDAM Reprints ISBA 2010012, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2010012
    Note: In : Journal of Productivity Analysis, vol. 33, no. 2, p. 97-107 (2010)
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    1. SIMAR, Léopold & WILSON, Paul W., 1998. "Productivity growth in industrialized countries," LIDAM Discussion Papers CORE 1998036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    9. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers," Economic Journal, Royal Economic Society, vol. 92(365), pages 73-86, March.
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    1. Mayer, Andreas & Zelenyuk, Valentin, 2014. "Aggregation of Malmquist productivity indexes allowing for reallocation of resources," European Journal of Operational Research, Elsevier, vol. 238(3), pages 774-785.
    2. Yubin Zheng & Md. Harun Ur Rashid & Abu Bakkar Siddik & Wei Wei & Syed Zabid Hossain, 2022. "Corporate Social Responsibility Disclosure and Firm’s Productivity: Evidence from the Banking Industry in Bangladesh," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    3. Oleg Badunenko & Daniel J. Henderson & Valentin Zelenyuk, 2017. "The Productivity of Nations," CEPA Working Papers Series WP022017, School of Economics, University of Queensland, Australia.
    4. Florens, Jean-Pierre & Schwarz, Maik & Van Bellegem, Sébastien, 2010. "Nonparametric Frontier Estimation from Noisy Data," IDEI Working Papers 625, Institut d'Économie Industrielle (IDEI), Toulouse.
    5. Shittu, Adebayo M. & Odine, Agatha I., 2014. "Agricultural Productivity Growth in Sub-Saharan Africa, 1990-2010: the role of Investment, Governance and Trade," Conference papers 332439, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    6. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    7. Andreas Mayer & Valentin Zelenyuk, 2018. "Aggregation of Individual Efficiency Measures and Productivity Indices," CEPA Working Papers Series WP012018, School of Economics, University of Queensland, Australia.
    8. Jakub Growiec, 2013. "On the measurement of technological progress across countries," Bank i Kredyt, Narodowy Bank Polski, vol. 44(5), pages 467-504.
    9. Andreas Mayer & Valentin Zelenyuk, 2014. "An Aggregation Paradigm for Hicks-Moorsteen Productivity Indexes," CEPA Working Papers Series WP012014, School of Economics, University of Queensland, Australia.
    10. Benjamin Hampf, 2016. "Efficiency and productivity measurement with persistent benchmarks," Economics Bulletin, AccessEcon, vol. 36(3), pages 1715-1721.
    11. Antonio Peyrache, 2013. "Multilateral productivity comparisons and homotheticity," Journal of Productivity Analysis, Springer, vol. 40(1), pages 57-65, August.
    12. Md. Harun Ur Rashid & Shah Asadullah Mohd. Zobair & Md. Asad Iqbal Chowdhury & Azharul Islam, 2020. "Corporate governance and banks’ productivity: evidence from the banking industry in Bangladesh," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 615-637, July.
    13. Martin Boďa & Mariana Považanová, 2020. "Productivity patterns in Europe: adaptation of the Malmquist index to measuring group performance and productivity change over time," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 949-989, November.
    14. Reza Fallahnejad & Mohammad Reza Mozaffari & Peter Fernandes Wanke & Yong Tan, 2024. "Nash Bargaining Game Enhanced Global Malmquist Productivity Index for Cross-Productivity Index," Games, MDPI, vol. 15(1), pages 1-21, January.
    15. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    16. Gloria O. Dzeha & Joshua Abor & Festus Turkson & Elikplimi Agbloyor, 2018. "Technical Efficiency and Technical Change in Africa: The Role of Money from the Diasporas," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(7), pages 177-177, July.

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