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Forecasting the South African Inflation Rate: On Asymmetric Loss and Forecast Rationality

Author

Listed:
  • Christian Pierdzioch

    (Helmut-Schmidt-University, Department of Economics, Holstenhofweg, Hamburg, Germany)

  • Monique B. Reid

    (Stellenbosch University, Department of Economics, Private Bag X1, Matieland, South Africa, 7602.)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

Abstract

Using forecasts of the inflation rate in South Africa, we study the rationality of forecasts and the shape of forecasters’ loss function. When we study micro-level data of individual forecasts, we find mixed evidence of an asymmetric loss function, suggesting that inflation forecasters are heterogeneous with respect to the shape of their loss function. We also find strong evidence that inflation forecasts are in line with forecast rationality. When we poolthe data, and study sectoral inflation forecasts of financial analysts, trade unions, and the business sector, we find evidence for asymmetry in the loss function, and against forecast rationality. Upon comparing the micro-level results with those for pooled and sectoral data, we conclude that forecast rationality should be assessed based on micro-level data, and that freer access to this data would allow more rigorous analysis and discussion of the information content of the surveys.

Suggested Citation

  • Christian Pierdzioch & Monique B. Reid & Rangan Gupta, 2014. "Forecasting the South African Inflation Rate: On Asymmetric Loss and Forecast Rationality," Working Papers 201475, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201475
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    Cited by:

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    2. 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.
    3. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    4. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    5. 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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    Keywords

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    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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