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A Measure of Price Pressures

Author

Listed:
  • Jackson, Laura E.

    (Bentley University)

  • Kliesen, Kevin L.

    (Federal Reserve Bank of St. Louis)

  • Owyang, Michael T.

    (Federal Reserve Bank of St. Louis)

Abstract

The Federal Reserve devotes significant resources to forecasting key economic variables such as real gross domestic product growth, employment, and inflation. The outlook for these variables also matters a great deal to businesses and financial market participants. The authors present a factor-augmented Bayesian vector autoregressive forecasting model that significantly outperforms both a benchmark random walk model and a pure time-series model. They then use these factors in an ordered probit model to develop the probability distribution over a 12-month horizon. One distribution assesses the probability that inflation will exceed 2.5 percent over the next year; they term this probability a price pressure measure. This price pressure measure would provide policymakers and markets with a quantitative assessment of the probability that average inflation over the next 12 months will be higher than the Fed’s long-term inflation target of 2 percent.

Suggested Citation

  • Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2015. "A Measure of Price Pressures," Review, Federal Reserve Bank of St. Louis, vol. 97(1), pages 25-52.
  • Handle: RePEc:fip:fedlrv:00035
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    File URL: https://files.stlouisfed.org/files/htdocs/publications/review/2015/q1/25-52JacksonKliesenOwyang.pdf
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    References listed on IDEAS

    as
    1. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters,in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. William T. Gavin & Kevin L. Kliesen, 2008. "Forecasting inflation and output: comparing data-rich models with simple rules," Review, Federal Reserve Bank of St. Louis, issue May, pages 175-192.
    4. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Luca Brugnolini, 2018. "Forecasting Deflation Probability in the EA: A Combinatoric Approach," CBM Working Papers WP/01/2018, Central Bank of Malta.
    2. Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2015. "Introducing the St. Louis Fed Price Pressures Measure," Economic Synopses, Federal Reserve Bank of St. Louis, issue 25.
    3. Hernández del Valle Gerardo, 2015. "On the pricing of defaultable bonds and Hitting times of Ito processes," Working Papers 2015-21, Banco de México.
    4. Lansing, Kevin J., 2017. "Endogenous Regime Switching Near the Zero Lower Bound," Working Paper Series 2017-24, Federal Reserve Bank of San Francisco.

    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
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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