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Macroeconomic forecasting during the Great Recession: The return of non-linearity?

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  • Ferrara, Laurent
  • Marcellino, Massimiliano
  • Mogliani, Matteo

Abstract

The debate on the forecasting ability of non-linear models has a long history, and the Great Recession episode provides an interesting opportunity for a re-assessment of the forecasting performances of several classes of non-linear models. An extensive analysis is performed over a broad cross-country database of the main macroeconomic indicators. The results suggest that, on average, non-linear models cannot outperform standard linear specifications, even during the Great Recession episode. However, non-linear models do lead to an improvement in predictive accuracy in almost 40%–45% of cases, and interesting specific patterns arise across models and variables, though in general the gains are limited. Overall, our findings are consistent with the hypothesis that describes this recent recession episode as a sequence of unusually large shocks, rather than as an increase in the degree of non-linearity in the stochastic processes underlying the main macroeconomic time series.

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  • Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:664-679
    DOI: 10.1016/j.ijforecast.2014.11.005
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    2. Caterina Forti Grazzini & Massimo Guidolin, 2013. "Forecasting yield spreads under crisis-induced multiple breakpoints," Applied Economics Letters, Taylor & Francis Journals, vol. 20(18), pages 1656-1664, December.
    3. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    4. Zanetti Chini, Emilio, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 34(4), pages 711-732.
    5. Boris Blagov & Michael Funke & Richhild Moessner, 2015. "Modelling the time-variation in euro area lending spreads," BIS Working Papers 526, Bank for International Settlements.
    6. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    7. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    8. Andrea Carriero & Galvao, Ana Beatriz & Kapetanios, George, 2016. "A comprehensive evaluation of macroeconomic forecasting methods," EMF Research Papers 10, Economic Modelling and Forecasting Group.
    9. Kurmaş Akdoğan, 2015. "Unemployment Hysteresis and Structural Change in Europe," EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey 266, Ekonomik Yaklasim Association.
    10. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    11. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
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    16. Pablo Guerron-Quintana & Molin Zhong, 2017. "Macroeconomic Forecasting in Times of Crises," Finance and Economics Discussion Series 2017-018, Board of Governors of the Federal Reserve System (U.S.), revised 31 Jan 2017.
    17. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    18. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    19. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    20. Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth with Financial Variables and Confidence Indicators," CBT Research Notes in Economics 1614, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    21. Kurmaş Akdoğan, 2015. "Asymmetric Behaviour of Inflation around the Target in Inflation-Targeting Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 486-504, November.
    22. Benjamin Garcia & Arsenios Skaperdas, "undated". "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.), revised 10 Dec 2019.

    More about this item

    Keywords

    Non-linear models; Forecast comparison; Global recession;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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