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Abstract:
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The U.S. Bureau of Labor Statistics (BLS) publish employment totals for all U.S. counties on a monthly basis. BLS use the Quarterly Census of Employment and Wages, where responses are received on a 6 ?? 7 month lagged basis and aggregated to county, and apply a time series forecast model to each county and project forward to the current month, which ignores the dependence among counties. Our approach treats these by- county employment time series as a collection of area indexed noisy functions that we co-model. We include predictor, trend and seasonality terms indexed by county. We demonstrate that use of a Fourier basis to model seasonality outperforms a locally- adaptive, intrinsic conditional autoregressive prior on our collection of time series. County-indexed parameters of the 3 terms are drawn from a dependent Dirichlet process (DDP) prior to allow the borrowing of information. We show that use of both spatial and industry concentration predictors produces better prediction accuracy. Our DDP prior accounts for the possibility that nearby counties may express distinct underlying economic structures. Our joint modeling framework computes efficiently to support production.
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