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Muth's Hypothesis Under Knightian Uncertainty: A Novel Account of Inflation Forecasts

Author

Listed:
  • Roman Frydman

    (Department of Economics, New York University)

  • Morten Nyboe Tabor

    (Institute for New Economic Thinking)

Abstract

We open a New Keynesian Phillips curve model to nonrecurring structural shifts in its parameters and propose a novel implementation of Muth's hypothesis to represent market participants' inflation expectations under Knightian uncertainty arising from such shifts. We refer to our approach as the Knight-Muth hypothesis (KMH). We find empirical support for KMH's core premise that processes driving inflation time-series and inflation forecasts undergo nonrecurring structural shifts. In contrast to the rational expectations hypothesis and behavioral specifications, KMH reconciles model consistency with an autonomous role for participants' expectations in driving aggregate outcomes and the influence of psychological factors on those expectations.

Suggested Citation

  • Roman Frydman & Morten Nyboe Tabor, 2022. "Muth's Hypothesis Under Knightian Uncertainty: A Novel Account of Inflation Forecasts," Working Papers Series inetwp194, Institute for New Economic Thinking.
    • Handle: RePEc:thk:wpaper:inetwp194
    DOI: 10.36687/inetwp194
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Expectations; Structural Shifts; Unforeseeable Change; Knightian Uncertainty; Muth's Hypothesis.;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • 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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