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The information content of regional employment data for forecasting aggregate conditions

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  • Rubén Hernández-Murillo
  • Michael T. Owyang

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://research.stlouisfed.org/wp/2004/2004-005.pdf
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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2004-005.

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Date of creation: 2004
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Publication status: Published in Economics Letters, March 2006, 90(3), pp. 339-5
Handle: RePEc:fip:fedlwp:2004-005

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Keywords: Econometrics ; Forecasting;

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  1. 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.
  2. Raffaella Giacomini & Clive W.J. Granger, 2002. "Aggregation of Space-Time Processes," Boston College Working Papers in Economics 582, Boston College Department of Economics.
  3. Lutkepohl, Helmut, 1984. "Linear transformations of vector ARMA processes," Journal of Econometrics, Elsevier, vol. 26(3), pages 283-293, December.
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Cited by:
  1. 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.
  2. 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.
  3. Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2008. "Regional unemployment forecasts with spatial interdependencies," IAB Discussion Paper 200828, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  4. Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2012. "Forecasting national recessions using state-level data," MPRA Paper 39168, University Library of Munich, Germany.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
  10. 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.

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