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Forecasting Aggregate Productivity Using Information from Firm-Level Data

Author

Listed:
  • Eric J. Bartelsman

    (VU University Amsterdam, Tinbergen Institute, and IZA)

  • Zoltan Wolf

    (U.S. Bureau of Census)

Abstract

In this paper, we explore whether information from firm-level data can improve forecasts of aggregate productivity growth. We generate firm-level productivity measures and aggregate them into time-series components that capture within-firm productivity and the productivity contribution of reallocation. We show that these components improve aggregate total factor productivity forecasts in a simple univariate setting, even when firm-level data are available with a time lag. Lagged firm-level information also improves aggregate productivity forecasts when we combine results from a variety of different multivariate forecasting models using Bayesian model averaging techniques. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology

Suggested Citation

  • Eric J. Bartelsman & Zoltan Wolf, 2014. "Forecasting Aggregate Productivity Using Information from Firm-Level Data," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 745-755, October.
    • Handle: RePEc:tpr:restat:v:96:y:2014:i:4:p:745-755
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    References listed on IDEAS

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    6. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    7. Gábor Kátay & Zoltán Wolf, 2008. "Driving Factors of Growth in Hungary - a Decomposition Exercise," MNB Working Papers 2008/6, Magyar Nemzeti Bank (Central Bank of Hungary).
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    Cited by:

    1. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    2. Christina Poetzsch, 2017. "Technology transfer on a two-way street: R&D spillovers through intermediate input usage and supply," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(4), pages 735-751, November.
    3. Ayyagari, Meghana & Demirguc-Kunt, Asli & Maksimovic, Vojislav, 2011. "Do Phoenix miracles exist ? firm-level evidence from financial crises," Policy Research Working Paper Series 5799, The World Bank.
    4. Jensen Christian, 2016. "On the macroeconomic effects of heterogeneous productivity shocks," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 1-23, January.
    5. Zhong, Sheng, 2016. "The dynamics of vehicle energy efficiency: Evidence from the Massachusetts Vehicle Census," MERIT Working Papers 2016-014, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. Jinchao Wang & Changfu Luo, 2022. "Social Mobility and Firms’ Total Factor Productivity: Evidence from China," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Pantea, Smaranda & Sabadash, Anna & Biagi, Federico, 2017. "Are ICT displacing workers in the short run? Evidence from seven European countries," Information Economics and Policy, Elsevier, vol. 39(C), pages 36-44.
    9. Fornaro, Paolo & Luomaranta, Henri, 2017. "Small and Medium Firms, Aggregate Productivity and the Role of Dependencies," ETLA Working Papers 47, The Research Institute of the Finnish Economy.
    10. Gorji, Narges Mirzaie & Fami, Hossein Shabanali & Iravani, Hooshang, 2017. "Investigating Factors that Affecting Citrus Waste Production in Mazandaran Province, Iran," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 7(1), March.

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    More about this item

    Keywords

    forecasting; aggregate; productivity; firm-level; Bayesian;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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