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Forecasting Productivity Using Information from Firm-Level Data

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Author Info
Eric J. Bartelsman () (VU University Amsterdam)
Zoltán Wolf () (VU University Amsterdam)

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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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Publisher 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
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Handle: RePEc:dgr:uvatin:20090043

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

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

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
D24 - Microeconomics - - Production and Organizations - - - Production; Capital and Total Factor Productivity; Capacity
O12 - Economic Development, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
O47 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Measurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
  2. Dale T. Mortensen & Rasmus Lentz, 2005. "An Empirical Model of Growth Through Product Innovation," 2005 Meeting Papers 910, Society for Economic Dynamics. [Downloadable!]
    Other versions:
  3. 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. [Downloadable!] (restricted)
  4. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Blackwell Publishing, vol. 70(2), pages 317-341, 04. [Downloadable!] (restricted)
    Other versions:
  5. 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. [Downloadable!] (restricted)
  6. John Geweke & Gianni Amisano, 2008. "Comparing and evaluating Bayesian predictive distributions of asset returns," Working Paper Series 969, European Central Bank. [Downloadable!]
  7. Ravazzolo, F. & Dijk, H.K. van & Verbeek, M.J.C.M., 2007. "Predictive gains from forecast combinations using time-varying model weights," Econometric Institute Report EI 2007-26 Revision_Date:, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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This page was last updated on 2009-11-26.

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