IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v96y2014i4p745-755.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00395
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jaimovich, Nir & Floetotto, Max, 2008. "Firm dynamics, markup variations, and the business cycle," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1238-1252, October.
    2. Ravazzolo, F. & van Dijk, H.K. & Verbeek, M.J.C.M., 2007. "Predictive gains from forecast combinations using time-varying model weights," Econometric Institute Research Papers EI 2007-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    4. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    5. 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).
    6. Charles R. Hulten, 1978. "Growth Accounting with Intermediate Inputs," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 45(3), pages 511-518.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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, 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.
    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. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sungki Hong, 2019. "Customer Capital, Markup Cyclicality, and Amplification," 2019 Meeting Papers 959, Society for Economic Dynamics.
    2. Konings, Jozef & Magerman, Glenn & Van Esbroeck, Dieter, 2023. "The impact of firm-level Covid rescue policies on productivity growth and reallocation," European Economic Review, Elsevier, vol. 157(C).
    3. Massimo Del Gatto & Adriana Di Liberto & Carmelo Petraglia, 2011. "Measuring Productivity," Journal of Economic Surveys, Wiley Blackwell, vol. 25(5), pages 952-1008, December.
    4. Amil Petrin & Jerome Reiter & Kirk White, 2011. "The Impact of Plant-level Resource Reallocations and Technical Progress on U.S. Macroeconomic Growth," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 3-26, January.
    5. Amil Petrin & James Levinsohn, 2005. "Measuring Aggregate Productivity Growth Using Plant-Level Data," NBER Working Papers 11887, National Bureau of Economic Research, Inc.
    6. Kamil Galuscak & Lubomir Lizal, 2011. "The Impact of Capital Measurement Error Correction on Firm-Level Production Function Estimation," Working Papers 2011/09, Czech National Bank.
    7. Han, Minsoo & Pyun, Ju Hyun, 2021. "Markups and income inequality: Causal links, 1975-2011," Journal of Comparative Economics, Elsevier, vol. 49(2), pages 290-312.
    8. Békés, Gábor & Halpern, László & Muraközy, Balázs, 2011. "A teremtő rombolás szerepe a vállalati termelékenység alakulásában Magyarországon [The role of creative destruction in the development of corporate productivity in Hungary]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 111-132.
    9. 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).
    10. Nicolò Gnocato & Concetta Rondinelli, 2018. "Granular sources of the Italian business cycle," Temi di discussione (Economic working papers) 1190, Bank of Italy, Economic Research and International Relations Area.
    11. Carlos D Santos & Luís F Costa & Paulo B Brito, 2022. "Demand, Supply and Markup Fluctuations," The Economic Journal, Royal Economic Society, vol. 132(644), pages 1620-1645.
    12. Christoph E. Boehm & Nitya Pandalai-Nayar, 2022. "Convex Supply Curves," American Economic Review, American Economic Association, vol. 112(12), pages 3941-3969, December.
    13. Kilinc, U., 2010. "Measuring competition in a frictional economy," Serie Research Memoranda 0012, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    14. Maican, Florin & Orth, Matilda, 2015. "A dynamic analysis of entry regulations and productivity in retail trade," International Journal of Industrial Organization, Elsevier, vol. 40(C), pages 67-80.
    15. Choudhry, Sonam, 2021. "Is India's formal manufacturing sector ‘hollowing out’- importance of intermediate input," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 533-547.
    16. David Greenstreet, 2007. "Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules," Economics Series Working Papers 345, University of Oxford, Department of Economics.
    17. Sungki Hong, 2017. "Customer Capital, Markup Cyclicality, and Amplification," Working Papers 2017-33, Federal Reserve Bank of St. Louis.
    18. Békés, Gábor & Harasztosi, Péter, 2013. "Agglomeration premium and trading activity of firms," Regional Science and Urban Economics, Elsevier, vol. 43(1), pages 51-64.
    19. David Baqaee & Emmanuel Farhi, 2020. "Entry vs. Rents: Aggregation with Economies of Scale," NBER Working Papers 27140, National Bureau of Economic Research, Inc.
    20. Lenzu, Simone & Manaresi, Francesco, 2018. "Do Marginal Products Differ from User Costs? Micro-Level Evidence from Italian Firms," Working Papers 276, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:96:y:2014:i:4:p:745-755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kelly McDougall (email available below). General contact details of provider: https://direct.mit.edu/journals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.