EMLGAPDec 1, 2025

Opening the Black Box: Nowcasting Singapore's GDP Growth and its Explainability

arXiv:2512.02092v11.2
Originality Synthesis-oriented
AI Analysis

This work provides timely economic assessments for policymakers in small, open economies like Singapore, though it is incremental as it applies existing methods to a specific domain.

The authors tackled the problem of nowcasting Singapore's quarterly GDP growth by developing a real-time framework using a high-dimensional panel of about 70 indicators, achieving RMSFE reductions of roughly 40-60% compared to benchmarks through methods like penalized regressions and GRU networks.

Timely assessment of current conditions is essential especially for small, open economies such as Singapore, where external shocks transmit rapidly to domestic activity. We develop a real-time nowcasting framework for quarterly GDP growth using a high-dimensional panel of approximately 70 indicators, encompassing economic and financial indicators over 1990Q1-2023Q2. The analysis covers penalized regressions, dimensionality-reduction methods, ensemble learning algorithms, and neural architectures, benchmarked against a Random Walk, an AR(3), and a Dynamic Factor Model. The pipeline preserves temporal ordering through an expanding-window walk-forward design with Bayesian hyperparameter optimization, and uses moving block-bootstrap procedures both to construct prediction intervals and to obtain confidence bands for feature-importance measures. It adopts model-specific and XAI-based explainability tools. A Model Confidence Set procedure identifies statistically superior learners, which are then combined through simple, weighted, and exponentially weighted schemes; the resulting time-varying weights provide an interpretable representation of model contributions. Predictive ability is assessed via Giacomini-White tests. Empirical results show that penalized regressions, dimensionality-reduction models, and GRU networks consistently outperform all benchmarks, with RMSFE reductions of roughly 40-60%; aggregation delivers further gains. Feature-attribution methods highlight industrial production, external trade, and labor-market indicators as dominant drivers of Singapore's short-run growth dynamics.

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