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Forecasting the Malmquist Productivity Index

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  • Daskovska, Alexandra
  • Simar, Léopold
  • Van Bellegem, Sébastien

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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Bibliographic Info

Paper provided by Toulouse School of Economics (TSE) in its series TSE Working Papers with number 09-048.

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Date of creation: 11 Jun 2009
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Handle: RePEc:tse:wpaper:22150

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Keywords: Malmquist Productivity Index; circularity efficiency; smooth bootstrap;

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  1. SIMAR, Léopold & WILSON, Paul W., 1998. "Productivity growth in industrialized countries," CORE Discussion Papers 1998036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Summers, Robert & Heston, Alan, 1991. "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988," The Quarterly Journal of Economics, MIT Press, vol. 106(2), pages 327-68, May.
  3. Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June.
  4. Oulton,Nicholas & O'Mahony,Mary, 1994. "Productivity and Growth," Cambridge Books, Cambridge University Press, number 9780521453455, October.
  5. 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.
  6. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
  7. Simar, L. & Wilson, P.W., 1999. "Statistical Inference in Nonparametric Frontier Models: the State of the Art," Papers 9904, Catholique de Louvain - Institut de statistique.
  8. Van Bellegem, Sebastien & von Sachs, Rainer, 2004. "Forecasting economic time series with unconditional time-varying variance," International Journal of Forecasting, Elsevier, vol. 20(4), pages 611-627.
  9. Finn R. Førsund, 2002. "On the circularity of the Malmquist productivity index," ICER Working Papers 29-2002, ICER - International Centre for Economic Research.
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Citations

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Cited by:
  1. Jakub Growiec, 2013. "On the measurement of technological progress across countries," Bank i Kredyt, National Bank of Poland, Economic Institute, vol. 44(5), pages 467-504.
  2. Valentin Zelenyuk & Andreas Mayer, 2013. "Aggregation of Malmquist productivity indexes allowing for reallocation of resources," CEPA Working Papers Series WP062013, School of Economics, University of Queensland, Australia.
  3. 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.
  4. Antonio Peyrache, 2013. "Multilateral productivity comparisons and homotheticity," Journal of Productivity Analysis, Springer, vol. 40(1), pages 57-65, August.

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