Lessons for Forecasting Unemployment in the United States: Use Flow Rates, Mind the Trend

Brent Meyer and Murat Tasci

Working Paper 2015-1
February 2015

Download the full text of this paper (316 KB) Adobe Acrobat symbol

This paper evaluates the ability of autoregressive models, professional forecasters, and models that incorporate unemployment flows to forecast the unemployment rate. We pay particular attention to flows-based approaches–the more reduced-form approach of Barnichon and Nekarda (2012) and the more structural method in Tasci (2012)–to generalize whether data on unemployment flows are useful in forecasting the unemployment rate. We find that any approach that considers unemployment inflow and outflow rates performs well in the near term. Over longer forecast horizons, Tasci (2012) appears to be a useful framework even though it was designed to be mainly a tool to uncover long-run labor market dynamics such as the "natural" rate. Its usefulness is amplified at specific points in the business cycle when the unemployment rate is away from the longer-run natural rate. Judgmental forecasts from professional economists tend to be the single best predictor of future unemployment rates. However, combining those guesses with flows-based approaches yields significant gains in forecasting accuracy.

JEL classification: E24; E32; J64; C53

Key words: unemployment forecasting, natural rate, unemployment flows, labor market search

The authors thank participants of the Midwest Economic Association Meetings (Evanston, 2013). The views expressed here are the authors' and not necessarily those of the Federal Reserve Banks of Cleveland and Atlanta or the Federal Reserve System. Any remaining errors are the authors' responsibility.
Please address questions regarding content to Brent Meyer, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, brent.meyer@atl.frb.org, or Murat Tasci, Research Department, Federal Reserve Bank of Cleveland, PO Box 6387, Cleveland, OH 44101-1387, murat.tasci@clev.frb.org.
Subscribe to receive e-mail notifications about new papers.