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Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors

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  • S. Béreau
  • V. Faubert
  • K. Schmidt

Abstract

In this paper, we study the fit and the predictive performance of the Phillips curve for euro area inflation with regard to different inflation series, time periods and predictor variables, notably different global factors. We compare the relative performance of a large set of alternative global factors in the Phillips curve, such as commodity prices, import prices, global consumer inflation, global economic slack and foreign demand. We find that traditional global indicators such as oil prices and import prices provide more accurate information for euro area headline inflation than global slack measures. In what regards the forecast ability of the Phillips curve for headline inflation, we show that it is unstable and depends strongly on the time period. Global factors provide only limited additional information for forecasting. In addition, we explore whether domestic demand and global factors are useful for analysing the entire conditional distribution of euro area inflation. We find that their impact varies across inflation quantiles (low vs. high inflation) and that inflation is more persistent at the low end of the distribution. We provide evidence that quantile information can lead to more accurate forecasts in periods of persistently low inflation.

Suggested Citation

  • S. Béreau & V. Faubert & K. Schmidt, 2018. "Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors," Working papers 663, Banque de France.
  • Handle: RePEc:bfr:banfra:663
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    References listed on IDEAS

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    Cited by:

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    2. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    3. Stefano Neri & Stefano Siviero, 2018. "The Non-Standard Monetary Policy Measures of the ECB: Motivations, Effectiveness and Risks," Credit and Capital Markets, Credit and Capital Markets, vol. 51(4), pages 513-560.
    4. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2021. "Domestic and Global Determinants of Inflation: Evidence from Expectile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 982-1001, August.
    5. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.
    6. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
    7. Behera, Harendra & Wahi, Garima & Kapur, Muneesh, 2018. "Phillips curve relationship in an emerging economy: Evidence from India," Economic Analysis and Policy, Elsevier, vol. 59(C), pages 116-126.
    8. Mutascu, Mihai, 2019. "Phillips curve in US: New insights in time and frequency," Research in Economics, Elsevier, vol. 73(1), pages 85-96.
    9. Álvarez, Luis J. & Correa-López, Mónica, 2020. "Inflation expectations in euro area Phillips curves," Economics Letters, Elsevier, vol. 195(C).
    10. Oinonen, Sami & Vilmi, Lauri, 2021. "Analysing euro area inflation outlook with the Phillips curve," BoF Economics Review 5/2021, Bank of Finland.
    11. Koester, Gerrit & Lis, Eliza & Nickel, Christiane & Osbat, Chiara & Smets, Frank, 2021. "Understanding low inflation in the euro area from 2013 to 2019: cyclical and structural drivers," Occasional Paper Series 280, European Central Bank.

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    More about this item

    Keywords

    Inflation; Forecasting; Phillips curve; Quantile regression.;
    All these keywords.

    JEL classification:

    • 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
    • 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

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