South African inflation modelling using bootstrapped long short-term memory methods.

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Abstract

Inflation is a critical economic series, and proper targeting is required for a stable economy. With the current economic conditions that the world has faced as a result of COVID-19, understanding the effects of this on economies is critical because it will guide policies. Recent research on South African inflation has focused on statistical modelling, specifically the ARFIMA, GARCH, and GJR-GARCH models. In this study, we extend this into deep learning and use the MSE, RMSE, RSMPE, MAE, and MAPE to assess performance. To test which model has better forecasts, we use the Diebold-Mariano test. According to the findings of this study, clustered bootstrap LSTM models outperform the previously used ARFIMA-GARCH and ARFIMA-GJR-GARCH models.© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.