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The information content of regional employment data for forecasting aggregate conditions

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  • 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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Bibliographic Info

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

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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. Giacomini, Raffaella & Granger, Clive W.J., 2001. "Aggregationn of Space-Time Processes," University of California at San Diego, Economics Working Paper Series qt77f76455, Department of Economics, UC San Diego.
  3. Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2004. "Business cycle phases in U.S. states," Working Papers 2003-011, Federal Reserve Bank of St. Louis.
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Cited by:
  1. 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].
  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. Kristie M. Engemann & Rubén Hernández-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.
  4. 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.
  5. 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.
  6. Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2012. "Forecasting national recessions using state level data," Working Papers 2012-013, Federal Reserve Bank of St. Louis.
  7. 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.
  8. 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.
  9. 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.
  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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