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A linear benchmark for forecasting GDP growth and inflation?

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  • Massimiliano Marcellino

    (IGIER and CEPR, IEP-Universitá Bocconi, Milan, Italy)

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.

Suggested Citation

  • Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:4:p:305-340
    DOI: 10.1002/for.1059
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    1. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    2. Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 2011. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 82-106, February.
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    4. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    5. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    6. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    7. Khan, Md. Tareq Ferdous & Kundu, Nobinkhor, 2012. "Future Contribution of Export and Import to GDP in Bangladesh: A Box-Jenkins Approach," MPRA Paper 65153, University Library of Munich, Germany, revised 15 Jun 2012.
    8. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    9. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models," Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
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