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Regional Aggregation in Forecasting: An Application to the Federal Reserve's Eighth District
HernÃ¡ndez-Murillo and Owyang (2006) showed that accounting for spatial correlations in regional data can improve forecasts of national employment. This paper considers whether the predictive advantage of disaggregate models remains when forecasting subnational data. The authors conduct horse races among several forecasting models in which the objective is to forecast regional- or state-level employment.