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Are There Any Reliable Leading Indicators for US Inflation and GDP Growth?

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  • Anindya BANERJEE
  • Massimiliano MARCELLINO

Abstract

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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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2002/21.

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Date of creation: 2002
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Handle: RePEc:eui:euiwps:eco2002/21

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  2. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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