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Common faith or parting ways? A time varying parameters factor analysis of euro-area inflation

Author

Listed:
  • Delle Monache

    (Bank of Italy)

  • Ivan Petrella

    (Department of Economics, Mathematics & Statistics, Birkbeck
    Bank of England)

  • Fabrizio Venditti

    (Bank of Italy)

Abstract

We analyze the interaction among the common and country specific components for the inflation rates in twelve euro area countries through a factor model with time varying parameters. The variation of the model parameters is driven by the score of the predictive likelihood, so that, conditionally on past data, the model is Gaussian and the likelihood function can be evaluated using the Kalman filter. The empirical analysis uncovers significant variation over time in the model parameters. We find that, over an extended time period, inflation persistence has fallen over time and the importance of common shocks has increased relatively to the idiosyncratic disturbances. According to the model, the fall in inflation observed since the sovereign debt crisis, is broadly a common phenomenon, since no significant cross country inflation differentials have emerged. Stressed countries, however, have been hit by unusually large shocks.

Suggested Citation

  • Delle Monache & Ivan Petrella & Fabrizio Venditti, 2015. "Common faith or parting ways? A time varying parameters factor analysis of euro-area inflation," Birkbeck Working Papers in Economics and Finance 1515, Birkbeck, Department of Economics, Mathematics & Statistics.
  • Handle: RePEc:bbk:bbkefp:1515
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    File URL: https://eprints.bbk.ac.uk/id/eprint/15264
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    References listed on IDEAS

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    Cited by:

    1. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    2. 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.
    3. 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.
    4. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    5. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    6. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parrága, Susana & Carvalho,, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
    7. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
    8. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.

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    More about this item

    Keywords

    inflation; time-varying parameters; score driven models; state space models; dynamics factor models.;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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