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The information content of regional employment data for forecasting aggregate conditions

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Listed author(s):
  • Hernandez-Murillo, Ruben
  • Owyang, Michael T.

Abstract

We consider whether disaggregated data enhances the efficiency of aggregate employment forecasts. We find that incorporating spatial interaction into a disaggregated forecasting model lowers the out-of-sample mean-squared-error from a univariate aggregate model by 70 percent at a two-year horizon.

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File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(05)00317-4
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Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 90 (2006)
Issue (Month): 3 (March)
Pages: 335-339

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Handle: RePEc:eee:ecolet:v:90:y:2006:i:3:p:335-339
Contact details of provider: Web page: http://www.elsevier.com/locate/ecolet

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  1. Lutkepohl, Helmut, 1984. "Linear transformations of vector ARMA processes," Journal of Econometrics, Elsevier, vol. 26(3), pages 283-293, December.
  2. 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.
  3. 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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Cited by:

  1. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
  2. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2015. "Forecasting National Recessions Using State‐Level Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 847-866, 08.
  3. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
  4. Buda, Rodolphe, 2008. "Estimation de l'emploi régional et sectoriel salarié français : application à l'année 2006
    [Estimation of the french salaried regional and sectoral employment: application to the year 2006]    ," MPRA Paper 34881, University Library of Munich, Germany.
  5. Michelle T. Armesto & Rubén Hernández-Murillo & Michael T. Owyang & Jeremy M. Piger, 2007. "Identifying asymmetry in the language of the Beige Book: a mixed data sampling approach," Working Papers 2007-010, Federal Reserve Bank of St. Louis.
  6. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper Series 15_12, The Rimini Centre for Economic Analysis, revised Oct 2012.
  7. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
  8. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  9. Kristie M. Engemann & Rubén Hernández-Murillo & Michael T. Owyang, 2011. "Regional aggregation in forecasting: an application to the Federal Reserve’s Eighth District," Review, Federal Reserve Bank of St. Louis, issue May, pages 207-222.
  10. Cai, Charlie X. & Kyaw, Khine & Zhang, Qi, 2012. "Stock index return forecasting: The information of the constituents," Economics Letters, Elsevier, vol. 116(1), pages 72-74.
  11. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
  12. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.

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