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

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  • Sihle Kubheka

    (University of Witswatersrand)

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.

Suggested Citation

  • Sihle Kubheka, 2023. "South African inflation modelling using bootstrapped long short-term memory methods," SN Business & Economics, Springer, vol. 3(7), pages 1-11, July.
    • Handle: RePEc:spr:snbeco:v:3:y:2023:i:7:d:10.1007_s43546-023-00490-9
    DOI: 10.1007/s43546-023-00490-9
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    References listed on IDEAS

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    1. Kevin S. Nell, 2018. "Re‐Examining the Role of Structural Change and Nonlinearities in a Phillips Curve Model for South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 86(2), pages 173-196, June.
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    3. Mehmet Balcilar & Rangan Gupta & Charl Jooste, 2016. "Analyzing South Africa’s inflation persistence using an ARFIMA model with Markov-switching fractional differencing parameter," Journal of Developing Areas, Tennessee State University, College of Business, vol. 50(1), pages 47-57, January-M.
    4. J.W. Fedderke & E. Schaling, 2005. "Modelling Inflation In South Africa: A Multivariate Cointegration Analysis," South African Journal of Economics, Economic Society of South Africa, vol. 73(1), pages 79-92, March.
    5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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