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Behavioral Heterogeneity in U.S. Inflation Dynamics

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Listed:
  • Adriana Cornea-Madeira
  • Cars Hommes
  • Domenico Massaro

Abstract

In this article we develop and estimate a behavioral model of inflation dynamics with heterogeneous firms. In our stylized framework there are two groups of price setters, fundamentalists and random walk believers. Fundamentalists are forward-looking in the sense that they believe in a present-value relationship between inflation and real marginal costs, while random walk believers are backward-looking, using the simplest rule of thumb, naive expectations, to forecast inflation. Agents are allowed to switch between these different forecasting strategies conditional on their recent relative forecasting performance. We estimate the switching model using aggregate and survey data. Our results support behavioral heterogeneity and the significance of evolutionary learning mechanism. We show that there is substantial time variation in the weights of forward-looking and backward-looking behavior. Although on average the majority of firms use the simple backward-looking rule, the market has phases in which it is dominated by either the fundamentalists or the random walk believers.

Suggested Citation

  • Adriana Cornea-Madeira & Cars Hommes & Domenico Massaro, 2019. "Behavioral Heterogeneity in U.S. Inflation Dynamics," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 288-300, April.
  • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:2:p:288-300
    DOI: 10.1080/07350015.2017.1321548
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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