Nowcasting Growth Using the Bayesian Structural Time Series Model: Application to Tanzania

Sunwoo Lee

No 2026/049, IMF Working Papers from International Monetary Fund

Abstract: In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term economic fluctuations, especially in countries with limited access to high-frequency growth data. This paper examines the application of the Bayesian Structural Time Series (BSTS) model to the case of nowcasting quarterly economic growth in Tanzania, leveraging a range of high-frequency economic indicators. The BSTS model provides a flexible framework that incorporates trends, seasonal variations, and regression effects, while its spike-and-slab variable selection helps identify relevant indicators. This paper outlines a framework for model selection and evaluation, including robustness checks and sensitivity analysis, and demonstrate the model’s relative performance. Additionally, the model’s capacity to adapt to longer forecast horizons and dynamic regressors enhances its utility for understanding growth trends in changing economic environments.

Keywords: Nowcasting; Bayesian models; economic activity; GDP; low-income countries; Bayesian Structural Time Series model; inclusion probability; IMF working papers; application of the Bayesian Structural Time Series; prediction error; Time series analysis; Sensitivity analysis; Agricultural sector; Sub-Saharan Africa; Global; Africa (search for similar items in EconPapers)
Pages: 27
Date: 2026-03-20
New Economics Papers: this item is included in nep-ets and nep-for
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