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Forecasting Aggregate Productivity using Information from Firm-Level Data

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Author Info

  • Eric J. Bartelsman

    (VU University Amsterdam)

  • Zoltan Wolf

    (VU University Amsterdam)

Abstract

This paper contributes to the productivity literature by using results from firm-level productivity studies to improve forecasts of macro-level productivity growth. The paper employs current research methods on estimating firm-level productivity to build times-series components that capture the joint dynamics of the firm-level productivity and size distributions. The main question of the paper is to assess whether the micro-aggregated components of productivity---the so-called productivity decompositions---add useful information to improve the performance of macro-level productivity forecasts. The paper explores various specifications of decompositions and various forecasting experiments. The result from these horse-races is that micro-aggregated components improve simple aggregate total factor productivity forecasts. While the results are mixed for richer forecasting specifications, the paper shows, using Bayesian model averaging techniques (BMA), that the forecasts using micro-level information were always better than the macro alternative.

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File URL: http://papers.tinbergen.nl/09043.pdf
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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 09-043/3.

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Date of creation: 14 May 2009
Date of revision:
Handle: RePEc:dgr:uvatin:20090043

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Web page: http://www.tinbergen.nl

Related research

Keywords: Economic growth; production function; total factor productivity; aggregation; firm-level data data; Bayesian analysis; forecasting;

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References

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  1. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Wiley Blackwell, vol. 70(2), pages 317-341, 04.
  2. Dale T. Mortensen & Rasmus Lentz, 2005. "An Empirical Model of Growth Through Product Innovation," 2005 Meeting Papers 910, Society for Economic Dynamics.
  3. 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.
  4. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-97, November.
  5. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
  6. Gábor Kátay & Zoltán Wolf, 2008. "Driving Factors of Growth in Hungary - a Decomposition Exercise," MNB Working Papers 2008/6, Magyar Nemzeti Bank (the central bank of Hungary).
  7. 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.
  8. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 0589, European Central Bank.
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Cited by:
  1. 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.

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