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Big data forecasting of South African inflation
Byron Botha1, Rulof Burger2,3, Kevin Kotzé3,4
1Codera Analytics, 42 Ennis Road, Parkview, Gauteng 2193 South Africa.
Empirical Economics
|November 21, 2022
Summary
Statistical learning and big data improve inflation forecasts, especially during crises. These advanced models show promise for predicting future inflationary pressures.
Area of Science:
- Economics
- Econometrics
- Data Science
Background:
- Accurate inflation forecasting is crucial for economic stability.
- Traditional time-series models face challenges with complex economic dynamics.
Purpose of the Study:
- To evaluate the effectiveness of statistical learning and big data in enhancing inflation forecast accuracy.
- To compare the performance of statistical learning models against traditional benchmarks.
Main Methods:
- Utilized a large dataset of disaggregated consumption prices.
- Employed a suite of statistical learning and traditional time-series models.
- Incorporated off-model information like electricity tariff adjustments and within-month data.
Main Results:
- Statistical learning models compete with benchmarks over medium to longer horizons.
- These models excel due to their ability to capture nonlinear relationships and select relevant predictors.
- Central bank's near-term forecasts are accurate, enhanced by external data.
Conclusions:
- Statistical learning offers a powerful approach to inflation forecasting, particularly in volatile periods.
- Integrating diverse data sources significantly improves forecast performance.
- Understanding key drivers of inflation is essential for policy.
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