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Inflation forecasts and forecaster herding: Evidence from South African survey data

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  • Pierdzioch, Christian
  • Reid, Monique B.
  • Gupta, Rangan

Abstract

We use South African survey data to study whether inflation forecasts are unbiased. Depending on a forecaster’s information set, we evaluate whether forecasts are biased due to forecaster herding. Forecaster herding is strong when a forecaster’s information set contains no information on the contemporaneous forecasts of others. When we randomly allocate forecasters into a group of early forecasters who can only observe the past forecasts of others and late forecasters who can also observe the contemporaneous forecasters of their predecessors, evidence of forecaster herding weakens. Evidence of forecaster (anti-)herding is strong and significant in times of high (low) inflation volatility.

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  • Pierdzioch, Christian & Reid, Monique B. & Gupta, Rangan, 2016. "Inflation forecasts and forecaster herding: Evidence from South African survey data," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 62(C), pages 42-50.
    • Handle: RePEc:eee:soceco:v:62:y:2016:i:c:p:42-50
    DOI: 10.1016/j.socec.2016.03.004
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    Cited by:

    1. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    2. Michael Pedersen, 2020. "Surveying the survey: What can we learn about the effects of monetary policy on inflation expectations?," Working Papers Central Bank of Chile 889, Central Bank of Chile.
    3. Pierdzioch, Christian & Reid, Monique B. & Gupta, Rangan, 2016. "Forecasting the South African inflation rate: On asymmetric loss and forecast rationality," Economic Systems, Elsevier, vol. 40(1), pages 82-92.
    4. Christian Pierdzioch & Monique B. Reid & Rangan Gupta, 2018. "On the directional accuracy of inflation forecasts: evidence from South African survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 884-900, April.
    5. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    6. Young Bin Ahn & Yoichi Tsuchiya, 2016. "Directional analysis of consumers’ forecasts of inflation in a small open economy: evidence from South Korea," Applied Economics, Taylor & Francis Journals, vol. 48(10), pages 854-864, February.
    7. Şule GÜNDÜZ & Seçkin YILDIRIM & Mübeccel Banu DURUKAN, 2020. "An Investigation of the Factors Affecting Inflation Perceptions: A Case Study on Business and Economics Undergraduate Students Abstract: The aim of this study is to investigate the factors behind the ," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(45).
    8. John B. Broughton & Bento J. Lobo, 2018. "Herding and anchoring in macroeconomic forecasts: the case of the PMI," Empirical Economics, Springer, vol. 55(3), pages 1337-1355, November.
    9. Christoph Buehren & Tim Meyer & Christian Pierdzioch, 2020. "Experimental Evidence on Forecaster (anti-) Herding in Sports Markets," MAGKS Papers on Economics 202038, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

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    More about this item

    Keywords

    Inflation rate; Forecasting; Forecaster herding;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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