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Are there any reliable leading indicators for US inflation and GDP growth?

  • Banerjee, Anindya
  • Marcellino, Massimiliano

In this paper we evaluate the relative merits of three approaches to information extraction from a large data set for forecasting, namely, the use of an automated model selection procedure, the adoption of a factor model, and single-indicator-based forecast pooling. The comparison is conducted using a large set of indicators for forecasting US inflation and GDP growth. We also compare our large set of leading indicators with purely autoregressive models, using an evaluation procedure that is particularly relevant for policy making. The evaluation is conducted both ex-post and in a pseudo real time context, for several forecast horizons, and using both recursive and rolling estimation. The results indicate a preference for simple forecasting tools, with a good relative performance of pure autoregressive models, and substantial instability in the leading characteristics of the indicators.

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

Volume (Year): 22 (2006)
Issue (Month): 1 ()
Pages: 137-151

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Handle: RePEc:eee:intfor:v:22:y:2006:i:1:p:137-151
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. Filip Keereman, 2003. "External assumptions, the international environment and the track record of the Commission Forecast," European Economy - Economic Papers 2008 - 2015 189, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
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  3. Filip Keereman, 1999. "The track record of the Commission forecasts," European Economy - Economic Papers 2008 - 2015 137, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
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  18. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
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