Charisios Grivas, Mikkel Mandrup, Orimar Sauri
arXiv 10 Aug 2026 · Econometrics
arXiv:2608.09213 · PDF · Extracted main text
The paper considers the problem of variable selection for forecasting electricity spot prices. High-dimensional methods such as LASSO and Elastic Net are widely used for this purpose, and while they exhibit strong predictive performance, their tendency to select over-parameterized models raises questions about interpretability. We evaluate the performance of six variable selection procedures, includingthe recently proposed Boosting Multiple Testing (BMT) method, using an extensive dataset from six regional electricity markets. We assess their performance in terms of both out-of-sample forecasting ac-curacy and model parsimony. We find that, although LASSO and Elastic Net achieve similar accuracy and outperform most screening alternatives, BMT matches their forecasting performance while using less than one-tenth as many variables. Our results reveal that BMT offers researchers and practitioners a substantially more interpretable and computationally efficient alternative to shrinkage methods, without any loss of forecasting accuracy. These findings suggest that the over-parameterization typically associated with regularization methods is not a necessary price for predictive accuracy in electricity price forecasting.
appendix boundary found by appendix_command · 82% 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 | Kapetanios, G., Sarafidis, V., & Ventouri, A (2026) Model selection in high-dimensional linear regression using boosting with multiple testing | 1.000 | 6 | 3 | 100% |
| 2 | Lago, J., Marcjasz, G., De Schutter, B., & Weron, R (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark | 0.965 | 10 | 3 | 90% |
| 3 | Ziel, F., & Weron, R (2018) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks | 0.956 | 8 | 3 | 88% |
| 4 | Chudik, A., Kapetanios, G., & Pesaran, M. H (2018) A one covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models | 0.928 | 4 | 3 | 100% |
| 5 | Diebold, F. X., & Mariano, R. S (1995) Comparing predictive accuracy | 0.737 | 3 | 2 | 100% |
| 6 | Grivas, C., Kapetanios, G., Psaradakis, Z., Sarafidis, V., Vavra, M.… (2026) Nonlinear boosting with multiple testing in high-dimensional generalised linear models with binary responses self | 0.644 | 2 | 2 | 100% |
| 7 | Uniejewski, B., Marcjasz, G., & Weron, R (2019) Understanding intraday electricity markets: Variable selection and very short-term price forecasting using lasso | 0.644 | 2 | 2 | 100% |
| 8 | Weron, R (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future | 0.644 | 2 | 2 | 100% |
| 9 | Sharifvaghefi, M (2025) Variable selection in linear regressions with possibly all strongly correlated covariates | 0.644 | 2 | 2 | 100% |
| 10 | Fan, J., Ke, Y., & Wang, K (2020) Factor-adjusted regularized model selection | 0.511 | 4 | 2 | 25% |
Showing the top 10 of 33 scored citations.