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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. N. Kundan Kishor & Evan F. Koenig, 2009. "VAR Estimation and Forecasting When Data Are Subject to Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 181-190, July.
  2. Garratt, A. & Lee, K. & Pesaran, M. H. & Shin, Y., 1998. "A Long-run Structural Macro-econometric Model of the UK," Cambridge Working Papers in Economics 9812, Faculty of Economics, University of Cambridge.
  3. M. Hashem Pesaran & Martin Weale, 2005. "Survey Expectations," CESifo Working Paper Series 1599, CESifo Group Munich.
  4. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 0633, European Central Bank.
  5. Athanasios Orphanides & Simon van Norden, 2001. "The Unreliability of Output Gap Estimates in Real Time," CIRANO Working Papers 2001s-57, CIRANO.
  6. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
  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. Athony Garratt & Kevin Lee & Mohammad Hashem Pesaran & Yongcheol Shin, 2001. "Forecast Uncertainties in Macroeconomics Modelling: An Application to the UK Economy," ESE Discussion Papers 64, Edinburgh School of Economics, University of Edinburgh.
  9. 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.
  10. Kevin Lee & Emi Mise & Kalvinder Shields & Tony Garratt, 2005. "Real time Representations of the Output Gap," Money Macro and Finance (MMF) Research Group Conference 2005 26, Money Macro and Finance Research Group.
  11. 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.
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  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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