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Expectations, Learning Gains, and Forecast Errors: Assessing Nonlinearities with a Functional Coefficient Approach

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  • Fabio Milani

Abstract

This paper investigates potential nonlinearities in the gain function, which, under adaptive learning, regulates the updating of agents' beliefs in response to recent forecast errors. I use data on professional survey forecasts to estimate nonparametric functional-coefficient regression models. The estimation results reveal nonlinearities in the relationships between expectations and forecast errors, which are indicative of nonlinear gain functions. Gains increase when forecast errors are historically large, and respond asymmetrically to past overpredictions and underpredictions. The findings suggest incorporating nonlinearities in the modeling of learning gains, instead of relying on the constant-gain assumption.

Suggested Citation

  • Fabio Milani, 2025. "Expectations, Learning Gains, and Forecast Errors: Assessing Nonlinearities with a Functional Coefficient Approach," CESifo Working Paper Series 12124, CESifo.
    • Handle: RePEc:ces:ceswps:_12124
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    References listed on IDEAS

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    1. George William Evans, 2001. "Expectations in Macroeconomics Adaptive versus Eductive Learning," Revue économique, Presses de Sciences-Po, vol. 52(3), pages 573-582.
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

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