Ignacio Garrón, Andrey Ramos
arXiv 6 Jan 2025 · Econometrics
arXiv:2501.03380 · PDF · DOI · OpenAlex · Extracted main text
Accurate tracking of anthropogenic carbon dioxide (CO2) emissions is crucial for shaping climate policies and meeting global decarbonization targets. However, energy consumption and emissions data are released annually and with substantial publication lags, hindering timely decision-making. This paper introduces a panel nowcasting framework to produce higher-frequency predictions of the state-level growth rate of per-capita energy consumption and CO2 emissions in the United States (U.S.). Our approach employs a panel mixed-data sampling (MIDAS) model to predict per-capita energy consumption growth, considering quarterly personal income, monthly electricity consumption, and a weekly economic conditions index as predictors. A bridge equation linking per-capita CO2 emissions growth with the nowcasts of energy consumption is estimated using panel quantile regression methods. A pseudo out-of-sample study (2009-2018), simulating the real-time data release calendar, confirms the improved accuracy of our nowcasts with respect to a historical benchmark. Our results suggest that by leveraging the availability of higher-frequency indicators, we not only enhance predictive accuracy for per-capita energy consumption growth but also provide more reliable estimates of the distribution of CO2 emissions growth.
appendix boundary found by appendix_command · 84% of the source is main text. Read the extracted text to check this.
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 | Fosten, J. and S. Nandi (2023) Nowcasting U.S. State-level CO2 Emissions and Energy Consumption | 1.000 | 24 | 5 | 100% |
| 2 | Adrian, T., N. Boyarchenko, and D. Giannone (2019, 9) (2019) Vulnerable Growth | 1.000 | 5 | 3 | 100% |
| 3 | Baumeister, C., D. Leiva-León, and E. Sims (2024) Tracking Weekly State-Level Economic Conditions | 1.000 | 5 | 3 | 100% |
| 4 | Ferrara, L., M. Mogliani, and J.-G. Sahuc (2022) High-frequency Monitoring of Growth at Risk | 0.874 | 9 | 2 | 100% |
| 5 | Koenker, R (2004) Quantile regression for longitudinal data | 0.874 | 5 | 2 | 100% |
| 6 | Foroni, C., M. Marcellino, and C. Schumacher (2015, 1) (2015) Unrestricted mixed data sampling (midas): Midas regressions with unrestricted lag polynomials | 0.811 | 4 | 2 | 100% |
| 7 | Mogliani, M. and A. Simoni (2021, 9) (2021) Bayesian midas penalized regressions: Estimation, selection, and prediction | 0.811 | 4 | 2 | 100% |
| 8 | Chuliá, H., I. Garrón, and J. M. Uribe (2024, 4) (2024) Daily growth at risk: Financial or real drivers? the answer is not always the same | 0.737 | 3 | 2 | 100% |
| 9 | Jensen, S (2021) Use of Machine Learning in Climate Econometrics | 0.737 | 3 | 2 | 100% |
| 10 | Azomahou, T., F. Laisney, and P. N. Van (2006) Economic Development and CO2 Emissions: A Nonparametric Panel Approach | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 83 scored citations.