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Nowcasting, Business Cycle Dating and the Interpretation of New Information when Real Time Data are Available

  • Kevin Lee
  • Nilss Olekalns
  • Kalvinder Shields

    ()

A canonical model is described which reflects the real time informational context of decision-making. Comparisons are drawn with ‘conventional’ models that incorrectly omit market-informed insights on future macroeconomic conditions and inappropriately incorporate information that was not available at the time. It is argued that conventional models are misspecified and misinterpret news. However, neither diagnostic tests applied to the conventional models nor typical impulse response analysis will be able to expose these deficiencies clearly. This is demonstrated through an analysis of quarterly US data 1968q4-2006q1. However, estimated real time models considerably improve out-of- sample forecasting performance, provide more accurate ‘nowcasts’ of the current state of the macroeconomy and provide more timely indicators of the business cycle. The point is illustrated through an analysis of the US recessions of 1990q3—1991q2 and 2001q1— 2001q4.

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File URL: http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp08-17.pdf
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Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 08/17.

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Date of creation: May 2008
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Handle: RePEc:lec:leecon:08/17
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  1. Athanasios Orphanides & Simon Van_Norden, 2000. "The Reliability of Output Gap Estimates in Real Time," Econometric Society World Congress 2000 Contributed Papers 0768, Econometric Society.
  2. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  3. Pesaran, M. Hashem & Weale, Martin, 2006. "Survey Expectations," Handbook of Economic Forecasting, Elsevier.
  4. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  5. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representations of the Output Gap," Birkbeck Working Papers in Economics and Finance 0619, Birkbeck, Department of Economics, Mathematics & Statistics.
  6. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
  7. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
  8. N. Kundan Kishor & Evan F. Koenig, 2005. "VAR estimation and forecasting when data are subject to revision," Working Papers 0501, Federal Reserve Bank of Dallas.
  9. Garratt, Anthony & Lee, Kevin & Pesaran, M. Hashem & Shin, Yongcheol, 2012. "Global and National Macroeconometric Modelling: A Long-Run Structural Approach," OUP Catalogue, Oxford University Press, number 9780199650460, March.
  10. A Garratt & K Lee & M H Pesaran & Yongcheol Shin, 2004. "Forecast Uncertainties in Macroeconomics Modelling: An Application to the UK Economy," ESE Discussion Papers 64, Edinburgh School of Economics, University of Edinburgh.
  11. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
  12. Brunner, Allan D, 2000. "On the Derivation of Monetary Policy Shocks: Should We Throw the VAR Out with the Bath Water?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(2), pages 254-79, May.
  13. Anthony Garratt & Kevin Lee & M. Hashem Pesaran & Yongcheol Shin, 2003. "A Long run structural macroeconometric model of the UK," Economic Journal, Royal Economic Society, vol. 113(487), pages 412-455, 04.
  14. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
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