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.
Highlights
Inflation targeting, as a preferred framework for monetary policy, has been adopted by several central banks since the 1990s (Naraidoo & Gupta 2010)
In order to estimate the seasonally adjusted autoregressive integrated moving average (SARIMA) model an autoregressivemoving average (ARMA) conditional least squares model was applied with the Gauss–Newton optimisation and Marquardt step method
The model optimisation criterion was based on the best F-statistic, Akaike information and Schwarz criterion (SC), which suggested that the SARIMA (1,0,1) × (0,0,12) model specification is the superior alternative to consider
Summary
As a preferred framework for monetary policy, has been adopted by several central banks since the 1990s (Naraidoo & Gupta 2010). To address the conflicting arguments above, this article will compare the forecasting ability of the non-linear autoregressive (NAR) networks with that of less complex models, such as the additive Holt–Winters exponential smoothing model and the SARIMA model, to account for the presence of inflation seasonality (Kinda 2013).
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