Abstract
The U.S. Bureau of Labor Statistics (BLS) publishes 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. Our model includes predictor, trend and seasonality terms indexed by county. This application is among the first in the U.S. Federal Statistical System to address the joint modeling of a collection of time series expressing heterogenous seasonality patterns between them. We demonstrate that use of a Fourier basis to model seasonality outperforms a locally-adaptive, intrinsic conditional autoregressive construction on our collection of time series where the degree of expressed seasonality varies. 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 employment of both spatial and industry concentration predictors into the prior probabilities for co-clustering among the counties produces better prediction accuracy. Our DDP prior accounts for the possibility that nearby counties may express distinct underlying economic structures. A feature of our joint modeling framework is that it computes efficiently to support the monthly BLS production cycle. We compare the performances of alternative formulations for the dependent Dirichlet process prior on monthly, county employment data from 2002–2016.
Highlights
The Local Area Unemployment Survey (LAUS) program of the U.S Bureau of Labor Statistics (BLS) publishes employment and unemployment totals for local areas, in-Bayesian Nonparametric Functional Mixture Estimation cluding counties, across all states in the U.S in coordination and partnership with the states
While covering much of the U.S population, the Current Employment Statistics (CES) survey excludes 1751 relatively sparsely populated counties of the total 3108 counties in the continental U.S The LAUS program utilizes the Quarterly Census of Employment and Wages (QCEW), a census instrument administered to all business establishments by BLS, for the purpose of collecting workplace employment information for the 1751 counties not covered by the CES, which we label “non-CES counties”
We employ a dependent Dirichlet process (DDP) prior framework of Muller et al (2011) that uses a subset of predictors to construct a prior formulation for county co-clustering that increases the prior probability for the assignment of two counties to the same cluster to the extent that they express similar predictor values relative to the Dirichlet process (DP)
Summary
The Local Area Unemployment Survey (LAUS) program of the U.S Bureau of Labor Statistics (BLS) publishes employment and unemployment totals for local areas, in-. The LAUS program applies a time series forecasting model to each non-CES county, separately, to project forward the QCEW employment to the current month. This approach ignores the dependence among the collection of county time series induced by similarities in their economic structures. We allow the data to discover dependence among the counties by performing probabilistic clustering of these county-indexed parameters to achieve a non-parametric mixture distribution for each of the time series terms where we mix over counties. We employ a dependent Dirichlet process (DDP) prior framework of Muller et al (2011) that uses a subset of predictors to construct a prior formulation for county co-clustering that increases the prior probability for the assignment of two counties to the same cluster to the extent that they express similar predictor values relative to the Dirichlet process (DP)
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