Donia Besher, Anirban Sengupta, Tanujit Chakraborty
arXiv 16 Jul 2025 · Econometrics
arXiv:2507.12276 · PDF · DOI · OpenAlex · Extracted main text
Forecasting Climate Policy Uncertainty (CPU) is essential as policymakers strive to balance economic growth with environmental goals. High levels of CPU can slow down investments in green technologies, make regulatory planning more difficult, and increase public resistance to climate reforms, especially during times of economic stress. This study addresses the challenge of forecasting the US CPU index by building the Bayesian Structural Time Series (BSTS) model with a large set of covariates, including economic indicators, financial cycle data, and public sentiments captured through Google Trends. The key strength of the BSTS model lies in its ability to efficiently manage a large number of covariates through its dynamic feature selection mechanism based on the spike-and-slab prior. To validate the effectiveness of the selected features of the BSTS model, an impulse response analysis is performed. The results show that macro-financial shocks impact CPU in different ways over time. Numerical experiments are performed to evaluate the performance of the BSTS model with exogenous variables on the US CPU dataset over different forecasting horizons. The empirical results confirm that BSTS consistently outperforms classical and deep learning frameworks, particularly for semi-long-term and long-term forecasts.
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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 | Cho, Chulyoung and Yang, Jinseok and Jang, Beakcheol (2024) Climate policy uncertainty and its impact on real estate market dynamics: A sectoral and regional analysis | 1.000 | 6 | 3 | 100% |
| 2 | Annicchiarico, Barbara and Carattini, Stefano and Fischer, Carolyn a… (2022) Business cycles and environmental policy: A primer | 1.000 | 5 | 3 | 100% |
| 3 | Scott, S. L. and Varian, H. R (2014) Predicting the Present with Bayesian Structural Time Series | 0.874 | 6 | 2 | 100% |
| 4 | Konstantinos Gavriilidis (2021) Measuring Climate Policy Uncertainty | 0.843 | 3 | 3 | 100% |
| 5 | Ma, Dandan and Zhang, Dayong and Guo, Kun and Ji, Qiang (2024) Coupling between global climate policy uncertainty and economic policy uncertainty | 0.843 | 3 | 3 | 100% |
| 6 | Obani, Pedi Chiemena and Gupta, Joyeeta (2016) The impact of economic recession on climate change: Eight trends | 0.843 | 3 | 3 | 100% |
| 7 | Hyndman, Rob J and Athanasopoulos, George (2018) Forecasting: principles and practice | 0.737 | 3 | 3 | 67% |
| 8 | Zhang, Wenwen and Chiu, Yi-Bin (2020) Do country risks influence carbon dioxide emissions? A non-linear perspective | 0.737 | 3 | 2 | 100% |
| 9 | Zhang, Ren Jie and Razzaq, Asif (2022) Influence of economic policy uncertainty and financial development on renewable energy consumption in the BRICST region | 0.737 | 3 | 2 | 100% |
| 10 | Baker, Scott R. and Bloom, Nicholas and Davis, Steven J (2016) Measuring Economic Policy Uncertainty | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 103 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks | 0.405 | 1 | 1 |