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Common and Idiosyncratic Inflation

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Abstract

In this note, we disentangle changes in prices due to economy-wide (common) shocks from changes in prices due to idiosyncratic shocks.

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  • Matteo Luciani, 2020. "Common and Idiosyncratic Inflation," FEDS Notes 2020-03-05, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfn:2020-03-05
    DOI: 10.17016/2380-7172.2508
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    1. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    2. Diewert, W Erwin, 1978. "Superlative Index Numbers and Consistency in Aggregation," Econometrica, Econometric Society, vol. 46(4), pages 883-900, July.
    3. Marlene Amstad & Simon M. Potter & Robert W. Rich, 2017. "The New York Fed Staff Underlying Inflation Gauge (UIG)," Economic Policy Review, Federal Reserve Bank of New York, issue 23-2, pages 1-32.
    4. Laurence Ball & Sandeep Mazumder, 2020. "The Nonpuzzling Behavior of Median Inflation," Central Banking, Analysis, and Economic Policies Book Series, in: Gonzalo Castex & Jordi Galí & Diego Saravia (ed.),Changing Inflation Dynamics,Evolving Monetary Policy, edition 1, volume 27, chapter 3, pages 049-070, Central Bank of Chile.
    5. Jushan Bai & Kunpeng Li, 2016. "Maximum Likelihood Estimation and Inference for Approximate Factor Models of High Dimension," The Review of Economics and Statistics, MIT Press, vol. 98(2), pages 298-309, May.
    6. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    7. Jim Dolmas, 2005. "Trimmed mean PCE inflation," Working Papers 0506, Federal Reserve Bank of Dallas.
    8. Jushan Bai & Peng Wang, 2015. "Identification and Bayesian Estimation of Dynamic Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 221-240, April.
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    Cited by:

    1. Barigozzi, Matteo & Massacci, Daniele, 2025. "Modelling large dimensional datasets with Markov switching factor models," Journal of Econometrics, Elsevier, vol. 247(C).
    2. Hie Joo Ahn & Matteo Luciani, 2021. "Relative prices and pure inflation since the mid-1990s," Finance and Economics Discussion Series 2021-069, Board of Governors of the Federal Reserve System (U.S.).
    3. Barigozzi, Matteo & Hallin, Marc & Luciani, Matteo & Zaffaroni, Paolo, 2024. "Inferential theory for generalized dynamic factor models," Journal of Econometrics, Elsevier, vol. 239(2).
    4. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2023. "Understanding trend inflation through the lens of the goods and services sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 751-766, August.
    5. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    6. Cristina Conflitti, 2020. "Alternative measures of underlying inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 593, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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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