Original Research
Complementing South African inflation surveys: A suitable forecasting tool
Journal of Economic and Financial Sciences | Vol 11, No 1 | a191 |
DOI: https://doi.org/10.4102/jef.v11i1.191
| © 2018 Chris Van Heerden, Andre Heymans, Yudhvir Seetharam
| This work is licensed under CC Attribution 4.0
Submitted: 09 February 2018 | Published: 30 April 2018
Submitted: 09 February 2018 | Published: 30 April 2018
About the author(s)
Chris van Heerden, Department of Economics, North-West University, South AfricaAndre Heymans, Department of Economics, North-West University, South Africa
Yudhvir Seetharam, School of Economic and Business Sciences, University of the Witwatersrand, South Africa
Abstract
Central banks currently perform inflation expectation surveys in order to better align their inflation expectations with that of the general public. However, surveys are time-consuming, complicated, expensive and not always accurate, thus compromising the credibility of these expectations. The complexity of inflation targeting and the difficulty of forecasting in real time can also cause policymakers to consider more basic models, which can lead to inexact forecasts. This article employs less complicated models, such as the seasonally adjusted autoregressive integrated moving average and Holt-Winters exponential smoothing models, to provide equally reliable forecasts. A more complex approach in the form of a non-linear autoregressive neural network process was also employed to model the strategic and rational manner in which the general public formulates their expectations. Overall, the forecast estimates provided by these models were superior when compared with the inflation expectations provided by the International Monetary Fund, South African Reserve Bank and Bureau for Economic Research.
Keywords
inflation expectations; forecasting; Holt-Winters; SARIMA; neural networks
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