Forecasting Aggregate Productivity Using Information from Firm-Level Data
研究了企业层面数据能否提升总体生产率增长的预测效果，发现企业生产率及其重新配置成分能改善预测，即使数据有滞后也有效。
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 showthat 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.