The Fall 2012, Fall 2013, and Fall 2015 editions of BPEA each featured papers that introduced new models for analyzing data released by the Bureau of Labor Statistics every month. These papers are no longer updated, but this page is preserved for archival purposes.

Weather-Adjusting Economic Data
Michael Boldin and Jonathan Wright, Fall 2015
In this paper, Michael Boldin and Jonathan Wright implement a statistical methodology for adjusting employment data for the effects of deviations in weather from seasonal norms, using several indicators of weather, including temperature and snowfall. This is distinct from “seasonal adjustment,” which only controls for normal variations in weather across the year. The authors’ weather-adjusted employment were updated monthly since from January 2016 until April 2019.

Unseasonal Seasonals?
Jonathan Wright, Fall 2013
In this paper, Jonathan Wright shows that the statistical authorities would produce more useful economic data if they altered their seasonal adjustment procedures to constrain seasonal factors to vary less over time than current practice. The paper has important implications for how to interpret month-to-month movements in all major U.S. economic time series. The author’s alternative seasonal adjustments to employment data were updated monthly from March 2014 to April 2019, and alongside weather-adjusted data from January 2016 to April 2019.

The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market
Regis Barnichon and Christopher Nekarda, Fall 2012
In this paper, Regis Barnichon and Christopher Nekarda explain how their model has consistently yielded more accurate unemployment predictions in the near-term than professional forecasters and other econometric models. Monthly forecasts were published between October 2012 and October 2015.