Big data forecasting of South African inflation

Byron Botha (), Rulof Burger (), Kevin Kotze (), Neil Rankin () and Daan Steenkamp
Additional contact information
Byron Botha: Codera
Rulof Burger: University of Stellenbosch
Kevin Kotze: University of Cape Town
Neil Rankin: Predictive Insights

No 41, ERSA Working Paper Series from Economic Research Southern Africa

Abstract: We investigate whether the use of machine learning techniques and big data can enhance the accuracy of inflation forecasts and our understanding of the drivers of South African inflation. We make use of a large dataset for the disaggregated prices of consumption goods and services to compare the forecasting performance of a suite of different statistical learning models to several traditional time series models. We find that the statistical learning models are able to compete with most benchmarks, but their relative performance is more impressive when the rate of inflation deviates from its steady state, as was the case during the recent COVID-19 lockdown, and where one makes use of a conditional forecasting function that allows for the use of future information relating to the evolution of the inflationary process. We find that the accuracy of the Reserve Bank’s near-term inflation forecasts compare favourably to those from the models considered, reflecting the inclusion of off-model information such as electricity tariff adjustments and within-month data. Lastly, we generate Shapley values to identify the most important contributors to future inflationary pressure and provide policymakers with information about the potential sources of future inflationary pressure.

Keywords: Micro-data; inflation; high dimensional regression; penalised likelihood; Bayesian methods; statistical learning (search for similar items in EconPapers)
JEL-codes: C10 C11 C52 C55 E31 (search for similar items in EconPapers)
Date: 2024-08
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Published in ERSA Working Paper Series, August 2024

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https://ersawps.org/index.php/working-paper-series/article/view/41/27 First version, 2024 (application/pdf)

Related works:
Journal Article: Big data forecasting of South African inflation (2023)
Working Paper: Big data forecasting of South African inflation (2022)
Working Paper: Big data forecasting of South African inflation (2022)
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