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Regional aggregation in forecasting: an application to the Federal Reserve’s Eighth District

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  • Kristie M. Engemann
  • Rubén Hernández-Murillo
  • Michael T. Owyang

Abstract

Hernández-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. For some models, the objective is to forecast using the sum of further disaggregated employment (i.e., forecasts of metropolitan statistical area [MSA]-level data are summed to yield state-level forecasts). The authors find that the spatial relationships between states have sufficient predictive content to overcome small increases in the number of estimated parameters when forecasting regional-level data; this is not always true when forecasting state- and regional-level data using the sum of MSA-level forecasts.

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Bibliographic Info

Article provided by Federal Reserve Bank of St. Louis in its journal Review.

Volume (Year): (2011)
Issue (Month): May ()
Pages: 207-222

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Handle: RePEc:fip:fedlrv:y:2011:i:may:p:207-222:n:v.93no.3

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Keywords: Economic forecasting ; Federal Reserve District; 8th;

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  1. Owyang, Michael T. & Piger, Jeremy M. & Wall, Howard J. & Wheeler, Christopher H., 2008. "The economic performance of cities: A Markov-switching approach," Journal of Urban Economics, Elsevier, vol. 64(3), pages 538-550, November.
  2. Lesage, James P & Magura, Michael, 1990. "Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 8(1), pages 127-35, January.
  3. Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2004. "Business cycle phases in U.S. states," Working Papers, Federal Reserve Bank of St. Louis 2003-011, Federal Reserve Bank of St. Louis.
  4. Conley, Timothy G. & Molinari, Francesca, 2007. "Spatial correlation robust inference with errors in location or distance," Journal of Econometrics, Elsevier, Elsevier, vol. 140(1), pages 76-96, September.
  5. Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2007. "A state-level analysis of the Great Moderation," Working Papers, Federal Reserve Bank of St. Louis 2007-003, Federal Reserve Bank of St. Louis.
  6. Raffaella Giacomini & Clive W.J. Granger, 2002. "Aggregation of Space-Time Processes," Boston College Working Papers in Economics, Boston College Department of Economics 582, Boston College Department of Economics.
  7. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, Elsevier, vol. 90(3), pages 335-339, March.
  8. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series, European Central Bank 0589, European Central Bank.
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