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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. Based on an extensive forecast evaluation we document notable forecast instabilities for most simple Phillips curves. Euro area inflation was particularly hard to forecast in the run-up to the Economic and Monetary Union and after the sovereign debt crisis, when the trends—and, for the latter period, also the amount of slack—were harder to pin down. Yet, some specifications outperform a univariate benchmark and point to the following lessons: (i) the key type of time variation to consider is an inflation trend; (ii) a simple filter-based output gap works well, but after the Great Recession it is outperformed by endogenously estimated slack or by “institutional” estimates; (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; and (v) averaging over a wide range of modelling choices helps.

Suggested Citation

  • Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
  • Handle: RePEc:eee:intfor:v:39:y:2023:i:1:p:364-390
    DOI: 10.1016/j.ijforecast.2021.12.001
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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 21-23R, Federal Reserve Bank of Cleveland, revised 15 Aug 2022.
    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. 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.
    8. 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.
    9. Luca Brugnolini & Giuseppe Ragusa, 2022. "Euro Area Deflationary Pressure Index," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 883-900, October.
    10. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
    11. 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

    Forecasting; Inflation; Phillips curve; Detrending; Time-varying parameters; Density forecast;
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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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