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Does the Phillips curve help to forecast euro area inflation?

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  • Bańbura, Marta
  • Bobeica, Elena

Abstract

We find that it does, but choosing the right specification is not trivial. We unveil notable model instability, with breaks in the performance of most simple Phillips curves. Euro area inflation was particularly hard to forecast in the run-up to the EMU and after the sovereign debt crisis, when the trend and for the latter period, also the amount of slack, were harder to pin down. Yet, some specifications outperform a univariate benchmark most of the time and are thus a useful element in a forecaster's toolkit. We base these conclusions on an extensive forecast evaluation over 1994 - 2018, an extraordinarily long period by euro area standards. We complement the analysis using real-time data over 2005-2018. As lessons for practitioners, we find that: (i) the key type of time variation to consider is an inflation trend; (ii) a simple filter-based output gap works well overall as a measure of economic slack, but after the Great Recession it is outperformed by endogenously estimated slack or by estimates from international economic institutions; (iii) external variables do not bring forecast gains; (iv) newer generation Phillips curve models with several time-varying features are a promising avenue for forecasting, especially when density forecasts are of interest, and finally, (v) averaging over a wide range of modelling choices offers some hedge against breaks in forecast performance. JEL Classification: C53, E31, E37

Suggested Citation

  • Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20202471
    Note: 810771
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    Cited by:

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    2. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    3. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    4. Brand, Claus & Obstbaum, Meri & Coenen, Günter & Sondermann, David & Lydon, Reamonn & Ajevskis, Viktors & Hammermann, Felix & Angino, Siria & Hernborg, Nils & Basso, Henrique & Hertweck, Matthias & Bi, 2021. "Employment and the conduct of monetary policy in the euro area," Occasional Paper Series 275, European Central Bank.
    5. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.
    6. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Panpan Zhu & Qingjie Zhou & Yinpeng Zhang, 2024. "Investor attention and consumer price index inflation rate: Evidence from the United States," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    8. Fontanari, Claudia & Palumbo, Antonella & Salvatori, Chiara, 2022. "The updated Okun method for estimation of potential output with alternative measures of labor underutilization," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 158-178.
    9. Stephen G. Hall & George S. Tavlas & Yongli Wang & Deborah Gefang, 2024. "Inflation forecasting with rolling windows: An appraisal," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 827-851, July.
    10. Boriss Siliverstovs, 2021. "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers 2021/01, Latvijas Banka.
    11. Luca Brugnolini & Giuseppe Ragusa, 2022. "Euro Area Deflationary Pressure Index," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 883-900, October.
    12. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
    13. Ciccarelli, Matteo & Darracq Pariès, Matthieu & Priftis, Romanos & Angelini, Elena & Bańbura, Marta & Bokan, Nikola & Fagan, Gabriel & Gumiel, José Emilio & Kornprobst, Antoine & Lalik, Magdalena & Mo, 2024. "ECB macroeconometric models for forecasting and policy analysis," Occasional Paper Series 344, European Central Bank.
    14. Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.

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    Keywords

    C53; E31; E37;
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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