Piersilvio De Bortoli, Davide Ferrari, Francesco Ravazzolo, Luca Rossini
arXiv 18 Feb 2026 · Econometrics
arXiv:2602.16527 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the Model Selection Confidence Set (MSCS) methodology for univariate time series models involving autoregressive and moving average components, and applies it to study model selection uncertainty in the Italian electricity load data. Rather than relying on a single model selected by an arbitrary criterion, the MSCS identifies a set of models that are statistically indistinguishable from the true data-generating process at a given confidence level. The size and composition of this set reveal crucial information about model selection uncertainty: noisy data scenarios produce larger sets with many candidate models, while more informative cases narrow the set considerably. To study the importance of each model term, we consider numerical statistics measuring the frequency with which each term is included in both the entire MSCS and in Lower Boundary Models (LBM), its most parsimonious specifications. Applied to Italian hourly electricity load data, the MSCS methodology reveals marked intraday variation in model selection uncertainty and isolates a collection of model specifications that deliver competitive short-term forecasts while highlighting key drivers of electricity load like intraday hourly lags, temperature, calendar effects and solar energy generation.
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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 | Chao Zheng and Davide Ferrari and Yuhong Yang (2019) MODEL SELECTION CONFIDENCE SETS BY LIKELIHOOD RATIO TESTING self | 0.737 | 3 | 2 | 100% |
| 2 | Ferrari, D. and Yang, Y (2015) Confidence sets for model selection by F-testing self | 0.737 | 3 | 2 | 100% |
| 3 | Kock, Anders Bredahl and Medeiros, Marcelo and Vasconcelos, Gabriel (2020) Penalized Time Series Regression | 0.511 | 2 | 1 | 100% |
| 4 | Kuiper, Rebecca and Hoijtink, Herbert and Silvapulle, Mervyn (2011) An Akaike-type information criterion for model selection under inequality constraints | 0.405 | 1 | 1 | 100% |
| 5 | Baki Billah and Rob J. Hyndman and Anne B. Koehler (2005) Empirical information criteria for time series forecasting model selection | 0.405 | 1 | 1 | 100% |
| 6 | Bühlmann, Peter and van de Geer, Sara (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications | 0.405 | 1 | 1 | 100% |
| 7 | Todd E. Clark and Kenneth D. West (2007) Approximately normal tests for equal predictive accuracy in nested models | 0.405 | 1 | 1 | 100% |
| 8 | V. Dordonnat and S.J. Koopman and M. Ooms and A. Dessertaine and J.… (2008) An hourly periodic state space model for modelling French national electricity load | 0.405 | 1 | 1 | 100% |
| 9 | Chen, Jiahua and Chen, Zehua (2008) Extended Bayesian information criteria for model selection with large model spaces | 0.405 | 1 | 1 | 100% |
| 10 | Enrich, Jacint and Li, Ruoyi and Mizrahi, Alejandro and Reguant, Mar (2024) Measuring the impact of time-of-use pricing on electricity consumption: Evidence from Spain | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 47 scored citations.