Forecasting Aggregate Productivity Using Information from Firm-Level Data

Eric Bartelsman and Zoltán Wolf

The Review of Economics and Statistics, 2014, vol. 96, issue 4, 745-755

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

Keywords: forecasting; aggregate; productivity; firm-level; Bayesian (search for similar items in EconPapers)
JEL-codes: E17 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (12)

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Working Paper: Forecasting Aggregate Productivity using Information from Firm-Level Data (2009)
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