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Understanding Trend Inflation Through the Lens of the Goods and Services Sectors

Author

Listed:
  • Yunjong Eo
  • Luis Uzeda
  • Benjamin Wong

Abstract

Monetary policy is largely concerned with managing the part of inflation that is persistent (or permanent), a quantity often referred to as trend inflation. For example, a casual reading of any monetary policy report from the Federal Reserve Board will make it clear that, in addition to total (or headline) inflation, the Federal Reserve also focuses on underlying (or core) measures of inflation that exclude more volatile components such as food and energy prices. This strategy is based on the belief that fluctuations in components such as food and energy prices are ultimately temporary and, consequently, should be excluded from monetary policy considerations about the long-run path of inflation. Trend inflation is thus closely related to the concept of core inflation, since both measures provide a reading on inflation without the transient “noise” that is expected to fade in the short run. A more recent development is that goods and services—the two main sectors used to measure inflation—have been experiencing considerably different dynamics over the past three decades. Our goal in this paper is to understand how such contrasting behaviors at the sectoral level affect the aggregate level of trend inflation dynamics. To do so, we develop an empirical framework that accounts for historical changes in the volatility and comovement of trend inflation in the goods and services sectors for the US. Our main finding is that, while both sectors used to contribute to the overall variation in aggregate trend inflation, since the 1990s variations in trend inflation have been almost entirely dominated by the services sector. Two changes in sector-specific inflation dynamics drive our key result: (i) a large fall in the variance of trend inflation in the goods sector; and (ii) the disappearance of comovement between the two sectors. We document similar findings when extending our analysis to Australia and Canada, suggesting our results are not US-specific.

Suggested Citation

  • Yunjong Eo & Luis Uzeda & Benjamin Wong, 2020. "Understanding Trend Inflation Through the Lens of the Goods and Services Sectors," Staff Working Papers 20-45, Bank of Canada.
  • Handle: RePEc:bca:bocawp:20-45
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    References listed on IDEAS

    as
    1. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2016. "A Bounded Model of Time Variation in Trend Inflation, Nairu and the Phillips Curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 551-565, April.
    2. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    3. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.
    4. Omori, Yasuhiro & Chib, Siddhartha & Shephard, Neil & Nakajima, Jouchi, 2007. "Stochastic volatility with leverage: Fast and efficient likelihood inference," Journal of Econometrics, Elsevier, vol. 140(2), pages 425-449, October.
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    More about this item

    Keywords

    Econometric and statistical methods; Inflation and prices; Monetary policy: transmission of;
    All these keywords.

    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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