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Evaluating Limited Influence Estimators of Core Inflation in New Zealand

Author

Listed:
  • Ryan Greenaway-McGrevy
  • James Jones

Abstract

Core inflation is commonly understood as the component in headline inflation that is expected to persist over the medium term. This paper evaluates the recent performance of limited influence estimators of CPI inflation in New Zealand over the 2000 to 2022 period. We show that an asymmetric trimmed mean that removes 19% of the sample from the lower tail and 20% from the upper tail minimizes squared errors when a conventional ex-post smoothed measure of headline inflation is used as a proxy for core inflation. These weights are very close to the 20% trimmed mean published by Statistics NZ, suggesting that this measure currently offers the most reliable measure of core inflation in the New Zealand context.

Suggested Citation

  • Ryan Greenaway-McGrevy & James Jones, 2022. "Evaluating Limited Influence Estimators of Core Inflation in New Zealand," Working Papers 010, University of Auckland, Economic Policy Center (EPC).
    • Handle: RePEc:cyc:wpaper:010
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    References listed on IDEAS

    as
    1. Michael F. Bryan & Christopher J. Pike, 1991. "Median price changes: an alternative approach to measuring current monetary inflation," Economic Commentary, Federal Reserve Bank of Cleveland, issue Dec.
    2. Laurence Ball & N. Gregory Mankiw, 1995. "Relative-Price Changes as Aggregate Supply Shocks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 161-193.
    3. Cogley, Timothy, 2002. "A Simple Adaptive Measure of Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 94-113, February.
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    More about this item

    Keywords

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    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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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