Structural Breaks and Comparative Time-Series Forecasting of Kenya’s Mobile Money Ecosystem
Lali Yusuf Ali *
Department of Mathematics, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Background: Kenya’s mobile money ecosystem has expanded substantially, but changes in its underlying growth rate and the forecasting implications of those changes warrant examination.
Problem: Policy-associated movements in the aggregate series may reflect longer-running structural changes rather than effects attributable to a particular intervention.
Aim: To detect structural breaks, assess the March 2020 emergency period and compare transaction-volume forecasts across horizons.
Methods: Monthly Central Bank of Kenya statistics covering July 2009–April 2026 (202 observations) were analysed. Chow, sup-F, dynamic-programming, CUSUM and variance-break tests examined stability. Seasonal ARIMA intervention models estimated adjusted period associations. Eight forecasting models were compared using expanding-window rolling-origin evaluation at 1-, 3-, 6- and 12-month horizons.
Results: Transaction-volume growth changed in May 2017 (sup-F = 13.72, P = .001), declining from approximately 2.2% to 0.5% monthly. CUSUM crossed its lower bound in June 2018; no robust variance break was identified. During March–December 2020, volume was 4.8% below its model-implied path (P = .006). The April–May 2020 window yielded a 13.1% reduction. Value and ticket-size effects were not statistically significant. Damped-trend exponential smoothing had the lowest forecast errors at every horizon (MAPE approximately 2.3%–3.1%).
Conclusion: Transaction-volume growth slowed before the 2018 policy changes, precluding attribution of the earlier break to those measures. The 2020 contraction was temporary, and forecast accuracy depended on accommodating trend deceleration.
Keywords: Average ticket size, CUSUM, Exponential smoothing, forecast evaluation, intervention analysis, Kenya, mobile money, rolling-origin forecasting, structural breaks, transaction volume