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The trend–cycle decomposition of output and the Phillips curve: Bayesian estimates for Italy and the Euro area

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  • Fabio Busetti

    (Bank of Italy)

  • Michele Caivano

    (Bank of Italy)

Abstract

A standard model-based trend–cycle decomposition of Italian GDP yields a likelihood function that is relatively flat. Bayesian estimation of the model allows to impose a mildly informative prior on the parameter governing the periodicity of the cycle, and thus, it helps to achieve the preferred decomposition. In a bivariate output and Phillips curve model for Italy, it is found that (i) the median response of prices to a 1 % shock to the output gap is equal to about 0.5 % after 20 quarters, (ii) the inflation cycle lags GDP on average by about three quarters. Estimating the model with Euro area data provides evidence of a smaller impact of the output gap on prices (0.4 %) and a lower lag of the inflation cycle with respect to GDP.

Suggested Citation

  • Fabio Busetti & Michele Caivano, 2016. "The trend–cycle decomposition of output and the Phillips curve: Bayesian estimates for Italy and the Euro area," Empirical Economics, Springer, vol. 50(4), pages 1565-1587, June.
    • Handle: RePEc:spr:empeco:v:50:y:2016:i:4:d:10.1007_s00181-015-0982-3
    DOI: 10.1007/s00181-015-0982-3
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    Cited by:

    1. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2019. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 541-569, September.
    2. Sune Karlsson & Pär Österholm, 2020. "A note on the stability of the Swedish Phillips curve," Empirical Economics, Springer, vol. 59(6), pages 2573-2612, December.
    3. Davide Fantino & Sara Formai & Alessandro Mistretta, 2021. "Firm characteristics and potential output: a growth accounting approach," Questioni di Economia e Finanza (Occasional Papers) 616, Bank of Italy, Economic Research and International Relations Area.
    4. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    5. Éva Gyurkovics & Tibor Takács, 2023. "Estimation of the potential GDP by a new robust filter method," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1183-1207, December.
    6. 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.

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General

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