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

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  • Kristie M. Engemann
  • Ruben Hernandez-Murillo
  • Michael T. Owyang

Abstract

Hernndez-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.

Suggested Citation

  • Kristie M. Engemann & Ruben Hernandez-Murillo & Michael T. Owyang, 2008. "Regional aggregation in forecasting: an application to the Federal Reserve's Eighth District," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Oct, pages 15-29.
    • Handle: RePEc:fip:fedlrd:y:2008:i:oct:p:15-29:n:v.4no.1
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    References listed on IDEAS

    as
    1. Hendry, David F & Hubrich, Kirstin, 2006. "Forecasting Economic Aggregates by Disaggregates," CEPR Discussion Papers 5485, C.E.P.R. Discussion Papers.
    2. Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2008. "A state-level analysis of the Great Moderation," Regional Science and Urban Economics, Elsevier, vol. 38(6), pages 578-589, November.
    3. Conley, Timothy G. & Molinari, Francesca, 2007. "Spatial correlation robust inference with errors in location or distance," Journal of Econometrics, Elsevier, vol. 140(1), pages 76-96, September.
    4. 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.
    5. Lesage, James P & Magura, Michael, 1990. "Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 127-135, January.
    6. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, vol. 90(3), pages 335-339, March.
    7. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
    8. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
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