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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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    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. 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.
    3. 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.
    4. Gorji, Narges Mirzaie & Fami, Hossein Shabanali & Iravani, Hooshang, . "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(01).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Jinchao Wang & Changfu Luo, 2022. "Social Mobility and Firms’ Total Factor Productivity: Evidence from China," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    10. Fabling Richard, 2024. "Still medalling: Productivity gets a bronze (data source)," Motu Working Papers 24_06, Motu Economic and Public Policy Research.
    11. 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).

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    Keywords

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    JEL classification:

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

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