FORECASTING AGGREGATE PRODUCTIVITY USING INFORMATION FROM FIRM-LEVEL DATA

• 成果类型: Article
• 署名作者: Bartelsman, Eric J.; Wolf, Zoltan
• 署名单位: Vrije Universiteit Amsterdam; Tinbergen Institute
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00395
• 发表日期: 2014-10
• 页码: 745-755
• 关键词: growth; DYNAMICS; MODEL

摘要

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.