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On the directional accuracy of inflation forecasts: evidence from South African survey data

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  • Christian Pierdzioch
  • Monique B. Reid
  • Rangan Gupta

Abstract

We study the information content of South African inflation survey data by determining the directional accuracy of both short-term and long-term forecasts. We use relative operating characteristic (ROC) curves, which have been applied in a variety of fields including weather forecasting and radiology, to ascertain the directional accuracy of the forecasts. A ROC curve summarizes the directional accuracy of forecasts by comparing the rate of true signals (sensitivity) with the rate of false signals (one minus specifity). A ROC curve goes beyond market-timing tests widely studied in earlier research as this comparison is carried out for many alternative values of a decision criterion that discriminates between signals (of a rising inflation rate) and nonsignals (of an unchanged or a falling inflation rate). We find consistent evidence that forecasts contain information with respect to the subsequent direction of change of the inflation rate.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:884-900
    DOI: 10.1080/02664763.2017.1322556
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    1. Reid, Monique, 2015. "Inflation expectations of the inattentive general public," Economic Modelling, Elsevier, vol. 46(C), pages 157-166.
    2. 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.
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    Cited by:

    1. Mr. Ken Miyajima & James Yetman, 2018. "Inflation Expectations Anchoring Across Different Types of Agents: the Case of South Africa," IMF Working Papers 2018/177, International Monetary Fund.
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    3. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    4. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    5. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
    6. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    7. Babalos, Vassilios & Stavroyiannis, Stavros & Gupta, Rangan, 2015. "Do commodity investors herd? Evidence from a time-varying stochastic volatility model," Resources Policy, Elsevier, vol. 46(P2), pages 281-287.

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    More about this item

    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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