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Macroeconomic forecasting with real-time data: an empirical comparison

  • Heij, C.
  • van Dijk, D.J.C.
  • Groenen, P.J.F.

Macroeconomic forecasting is not an easy task, in particular if future growth rates are forecasted in real time. This paper compares various methods to predict the growth rate of US Industrial Production (IP) and of the Composite Coincident Index (CCI) of the Conference Board, over the coming month, quarter, and half year. It turns out that future IP growth rates can be forecasted in real time from ten leading indicators, by means of the Composite Leading Index (CLI) or, even somewhat better, by principal components regression. This amends earlier negative findings for IP by Diebold and Rudebusch. For CCI, on the other hand, simple autoregressive models do not provide significantly less accurate forecasts than single-equation and bivariate vector autoregressive models with the CLI. This amends some of the more positive results for CCI recently reported by the Conference Board. Not surprisingly, all forecast methods improve considerably if `ex post' data are used, after possible data updates and revisions.

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File URL: http://repub.eur.nl/pub/17018/EI2009-27.pdf
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Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2009-27.

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Date of creation: 19 Oct 2009
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Handle: RePEc:ems:eureir:17018
Contact details of provider: Postal: Postbus 1738, 3000 DR Rotterdam
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Web page: http://www.eur.nl/ese

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  1. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
  2. Anindya Banerjee & Massimiliano Marcellino, 2003. "Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?," Working Papers 236, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  3. Pesaran, M.H. & Timmermann, A., 2004. "‘Real Time Econometrics’," Cambridge Working Papers in Economics 0432, Faculty of Economics, University of Cambridge.
  4. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
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  6. Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2010. "Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 6-28.
  7. Swanson, N.R. & van Dijk, D.J.C., 2001. "Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry," Econometric Institute Research Papers EI 2001-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  8. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  9. Tom Stark and Dean Croushore, 2001. "Forecasting with a Real-Time Data Set for Macroeconomists," Computing in Economics and Finance 2001 258, Society for Computational Economics.
  10. Robert H. McGuckin & Ataman Ozyildirim & Victor Zarnowitz, 2001. "The Composite Index of Leading Economic Indicators: How to Make It More Timely," NBER Working Papers 8430, National Bureau of Economic Research, Inc.
  11. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  12. Wesley Clair Mitchell & Arthur F. Burns, 1938. "Statistical Indicators of Cyclical Revivals," NBER Books, National Bureau of Economic Research, Inc, number mitc38-1, December.
  13. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
  14. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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