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

TL;DR

This paper develops a real-time nowcasting framework for Singapore's quarterly GDP growth using advanced machine learning models and explainability tools, achieving significant predictive improvements and identifying key economic drivers.

Contribution

It introduces a comprehensive, high-dimensional nowcasting approach combining multiple models with explainability, tailored for small open economies like Singapore.

Findings

Penalized regressions and neural networks outperform benchmarks with 40-60% RMSFE reduction.

Aggregation of models enhances predictive accuracy.

Feature attribution highlights trade, industry, and labor indicators as key growth drivers.

Abstract

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.