arXiv 10 Mar 2025 · Econometrics · publishedEnergy Economics (2025)
arXiv:2503.07213 · PDF · DOI · OpenAlex · Extracted main text
We estimate the temperature sensitivity of residential electricity demand during extreme temperature events using the distribution-to-scalar regression model. Rather than relying on simple averages or individual quantile statistics of raw temperature data, we construct distributional summaries, such as probability density, hazard rate, and quantile functions, to retain a more comprehensive representation of temperature variation. This approach not only utilizes richer information from the underlying temperature distribution but also enables the examination of extreme temperature effects that conventional models fail to capture. Additionally, recognizing that distribution functions are typically estimated from limited discrete observations and may be subject to measurement errors, our econometric framework explicitly addresses this issue. Empirical findings from the hazard-to-demand model indicate that residential electricity demand exhibits a stronger nonlinear response to cold waves than to heat waves, while heat wave shocks demonstrate a more pronounced incremental effect. Moreover, the temperature quantile-to-demand model produces largely insignificant demand response estimates, attributed to the offsetting influence of two counteracting forces.
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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 | Chang, Y., Kim, C.S., Miller, J.I., Park, J.Y., Park, S (2016) A new approach to modeling the effects of temperature fluctuations on monthly electricity demand | 1.000 | 6 | 3 | 100% |
| 2 | Chang, Y., Kim, C.S., Miller, J.I., Park, J.Y., Park, S (2014) Time-varying long-run income and output elasticities of electricity demand with an application to Korea | 1.000 | 5 | 4 | 100% |
| 3 | Chen, C., Guo, S., Qiao, X (2022) Functional linear regression: Dependence and error contamination | 0.928 | 5 | 3 | 80% |
| 4 | Seong, D., Seo, W.K (2025) Functional instrumental variable regression with an application to estimating the impact of immigration on native wages self | 0.679 | 32 | 3 | 31% |
| 5 | Benatia, D., Carrasco, M., Florens, J.P (2017) Functional linear regression with functional response | 0.644 | 3 | 2 | 67% |
| 6 | Yang, H., Baladandayuthapani, V., Rao, A.U., Morris, J.S (2020) Quantile function on scalar regression analysis for distributional data | 0.644 | 2 | 2 | 100% |
| 7 | Petersen, A., Müller, H.G (2016) Functional data analysis for density functions by transformation to a Hilbert space | 0.585 | 3 | 1 | 100% |
| 8 | Bosq, D (2000) Linear Processes in Function Spaces | 0.511 | 4 | 2 | 25% |
| 9 | Seo, W.K., Beare, B.K (2019) Cointegrated linear processes in Bayes Hilbert space self | 0.511 | 2 | 1 | 100% |
| 10 | van den Boogaart, K.G., Egozcue, J.J., Pawlowsky-Glahn, V (2014) Bayes Hilbert spaces | 0.405 | 1 | 1 | 100% |
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