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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.

Suggested Citation

  • Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
  • Handle: RePEc:eee:intfor:v:27:y::i:2:p:466-481
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    Cited by:

    1. Lise Pichette & Marie-Noëlle Robitaille, 2017. "Assessing the Business Outlook Survey Indicator Using Real-Time Data," Discussion Papers 17-5, Bank of Canada.
    2. Accetturo, Antonio & Bugamelli, Matteo & Lamorgese, Andrea R., 2013. "Skill upgrading and exports," Economics Letters, Elsevier, pages 417-420.
    3. Franses, Ph.H.B.F. & Janssens, E., 2017. "Spurious Principal Components," Econometric Institute Research Papers EI2017-31, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.
    5. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2014. "Evaluating Macroeconomic Forecasts: A Concise Review Of Some Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 195-208, April.
    6. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, pages 79-99.
    7. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, pages 847-862.
    8. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.

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