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Real-time macroeconomic forecasting with leading indicators: An empirical comparison

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  • Heij, Christiaan
  • van Dijk, Dick
  • Groenen, Patrick J.F.

Abstract

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989–2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 ()
Pages: 466-481

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Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:466-481

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Vintage data; Leading indicators; Forecast evaluation; Recessions; Industrial production; Composite Coincident Index;

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Citations

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Cited by:
  1. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.
  2. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2012. "Evaluating Macroeconomic Forecasts: A Concise Review of Some Recent Developments," Documentos de Trabajo del ICAE 2012-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  3. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
  4. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.

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