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Professionals Forecasting Inflation: The Role of Inattentiveness and Uncertainty

Author

Listed:
  • Easaw, Joshy

    (Cardiff Business School)

  • Golinelli, Roberto

    (Department of Economics, University of Bologna, ITALY)

  • Heravi, Saeed

    (Cardiff Business School)

Abstract

The purpose of this paper is to investigate the nature of professionals inflation forecasts inattentiveness. We introduce and empirically investigate a new generalized model of inattentiveness due to informational rigidity. In doing so, we outline a novel model that considers the non-linear relationship between inattentiveness and aggregate uncertainty, which crucially distinguishes between macro-economic and data (measurement error) uncertainty. The empirical analysis uses the Survey of Professional Forecasters data and indicates that inattentiveness due to imperfect information explains professional forecasts dynamics.

Suggested Citation

  • Easaw, Joshy & Golinelli, Roberto & Heravi, Saeed, 2022. "Professionals Forecasting Inflation: The Role of Inattentiveness and Uncertainty," Cardiff Economics Working Papers E2022/7, Cardiff University, Cardiff Business School, Economics Section.
    • Handle: RePEc:cdf:wpaper:2022/7
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    References listed on IDEAS

    as
    1. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    2. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    3. V. Haggan & S. M. Heravi & M. B. Priestley, 1984. "A Study Of The Application Of State‐Dependent Models In Non‐Linear Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(2), pages 69-102, March.
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    More about this item

    Keywords

    Forecasting Popular Votes Shares; Electoral Poll; Forecast combination; Hybrid model; Support Vector Machine;
    All these keywords.

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