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

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  • Laurent Ferrara

    () (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Massimiliano Marcellino
  • Matteo Mogliani

Abstract

The debate on the forecasting ability of non-linear models has a long history, and the Great Recession episode provides us with an interesting opportunity for a reassessment of the forecasting performance of several classes of non-linear models. We conduct an extensive analysis over a large quarterly database consisting of major macroeconomic variables for a large panel of countries. It turns out that, on average, non-linear models cannot outperform standard linear specifications, even during the Great Recession. However, non-linear models lead to an improvement of the predictive accuracy in almost 40% of cases, and interesting specific patterns emerge among models, variables and countries. These results suggest that this specific episode seems to be characterized by a sequence of shocks with unusual large magnitude, rather than by an increase in the degree of non-linearity of the stochastic processes underlying the main macroeconomic time series.
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  • Laurent Ferrara & Massimiliano Marcellino & Matteo Mogliani, 2015. "Macroeconomic forecasting during the Great Recession: the return of non-linearity?," Post-Print hal-01385973, HAL.
  • Handle: RePEc:hal:journl:hal-01385973
    Note: View the original document on HAL open archive server: https://hal-univ-paris10.archives-ouvertes.fr/hal-01385973
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    3. repec:spr:empeco:v:53:y:2017:i:4:d:10.1007_s00181-016-1171-8 is not listed on IDEAS
    4. Emilio Zanetti Chini, 2803. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," CREATES Research Papers 2018-13, Department of Economics and Business Economics, Aarhus University.
    5. Emilio Zanetti Chini, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," DEM Working Papers Series 156, University of Pavia, Department of Economics and Management.
    6. Boris Blagov & Michael Funke & Richhild Moessner, 2015. "Modelling the time-variation in euro area lending spreads," BIS Working Papers 526, Bank for International Settlements.
    7. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," EconomiX Working Papers 2014-21, University of Paris Nanterre, EconomiX.
    8. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    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. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    12. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    13. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    14. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    15. 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.
    16. 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.
    17. 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.).
    18. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.

    More about this item

    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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