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A linear benchmark for forecasting GDP growth and inflation?

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Author Info
Massimiliano Marcellino (IGIER and CEPR, IEP-Universitá Bocconi, Milan, Italy)

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Abstract

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory-based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1059
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 27 (2008)
Issue (Month): 4 ()
Pages: 305-340
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Handle: RePEc:jof:jforec:v:27:y:2008:i:4:p:305-340

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Papers 367, University of Pittsburgh, Department of Economics, revised Sep 2008. [Downloadable!]
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This page was last updated on 2009-12-10.

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