arXiv 1 Dec 2025 · Econometrics
arXiv:2512.02092 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zou, Hui and Hastie, Trevor (2005) Regularization and variable selection via the elastic net | 1.000 | 12 | 5 | 100% |
| 2 | Smeekes, Stephan and Wijler, Evie (2018) Macroeconomic forecasting using penalized regression methods | 1.000 | 12 | 4 | 100% |
| 3 | Akiba, Takuya and Sano, Shotaro and Yanase, Takahiko and Ohta, Taker… (2019) Optuna: A next-generation hyperparameter optimization framework | 1.000 | 12 | 3 | 100% |
| 4 | Bergstra, James and Bardenet, Rémi and Bengio, Yoshua and Kégl, Balázs (2011) Algorithms for hyper-parameter optimization | 1.000 | 12 | 3 | 100% |
| 5 | Hansen, Peter R. and Lunde, Asger and Nason, James M (2011) The model confidence set | 1.000 | 11 | 5 | 100% |
| 6 | Breiman, Leo (2001) Random Forests | 1.000 | 11 | 4 | 100% |
| 7 | Tashman, Leonard J (2000) Out-of-sample tests of forecasting accuracy: An analysis and review | 1.000 | 11 | 4 | 100% |
| 8 | Bańbura, Marta and Rünstler, Gerhard (2011) A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP | 1.000 | 10 | 4 | 100% |
| 9 | Timmermann, Allan G (2006) Forecast combinations | 1.000 | 10 | 4 | 100% |
| 10 | Hewamalage, Hansika and Bergmeir, Christoph and Bandara, Kasun (2021) Recurrent neural networks for time series forecasting: Current status and future directions | 1.000 | 9 | 6 | 100% |
Showing the top 10 of 232 scored citations.