The Information Content of Regional Employment Data for Forecasting Aggregate Conditions
Social Science Research Network
We consider whether disaggregated data enhances the efficiency of aggregate employment forecasts. We find that incorporating spatial interaction into a disaggregated forecasting model lowers the out-of-sample mean-squared-error from a univariate aggregate model by 70 percent at a five-year horizon. [JEL: C21, C53] Employment is a commonly used indicator of the state of the economy, and forecasts of employment are often used to signal the end of recessions. However, aggregate forecasts of
... forecasts of employment may not incorporate all available information. A few academic studies have assessed the viability of regional data as a means of forecasting aggregate variables. 1 Possibly, the assumption is that aggregate data is merely summed regional data, leaving the information content essentially equivalent. 2 We consider whether using disaggregated data can enhance the efficiency of aggregate employment forecasts. Recently, Owyang, Piger, and Wall (2003) used coincident indices constructed primarily from employment data in order to date state-level recessions. They suggest that information in the pattern of disaggregated recession propagation can be informative to policymaking at an aggregate level. The * This paper benefited from conversations with Leora Friedberg, Raffaella Giacomini, Jeremy Piger, and Howard Wall. Kristie M. Engemann and Deborah Roisman provided research assistance. The opinions expressed in this paper are solely those of the authors and do not in any way represent the views of the Federal Reserve Bank of St. Louis or the Federal Reserve System. The usual disclaimers apply. † coreesponding author: firstname.lastname@example.org 1 Lütkepohl (1984), for example. develops a theoretical framework for assessing the efficiency of some regional forecasting models.