Claudia Foroni, Francesco Ravazzolo, Luca Rossini
arXiv 24 Jul 2020 · Finance — Statistical Finance · publishedEconomic Modelling (2022) · 13 citations (OpenAlex)
arXiv:2007.13566 · PDF · DOI · OpenAlex · Extracted main text
Recent research finds that forecasting electricity prices is very relevant. In many applications, it might be interesting to predict daily electricity prices by using their own lags or renewable energy sources. However, the recent turmoil of energy prices and the Russian-Ukrainian war increased attention in evaluating the relevance of industrial production and the Purchasing Managers' Index output survey in forecasting the daily electricity prices. We develop a Bayesian reverse unrestricted MIDAS model which accounts for the mismatch in frequency between the daily prices and the monthly macro variables in Germany and Italy. We find that the inclusion of macroeconomic low frequency variables is more important for short than medium term horizons by means of point and density measures. In particular, accuracy increases by combining hard and soft information, while using only surveys gives less accurate forecasts than using only industrial production data.
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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 | Gianfreda, A., Ravazzolo, F., and Rossini, L (2020) Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration self | 0.928 | 4 | 3 | 100% |
| 2 | Foroni, C., Guérin, P., and Marcellino, M (2018) Using low frequency information for predicting high frequency variables self | 0.737 | 3 | 2 | 100% |
| 3 | Foroni, C., Ravazzolo, F., and Ribeiro, P (2015) Forecasting commodity currencies: the role of fundamentals with short-lived predictive content self | 0.644 | 2 | 2 | 100% |
| 4 | Pettenuzzo, D., Timmermann, A., and Valkanov, R (2016) A MIDAS approach to modeling first and second moment dynamics | 0.644 | 2 | 2 | 100% |
| 5 | Raviv, E., Bouwman, K. E., and van Dijk, D (2015) Forecasting day-ahead electricity prices: Utilizing hourly prices | 0.644 | 2 | 2 | 100% |
| 6 | Weron, R. and Misiorek, A (2008) Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models | 0.644 | 2 | 2 | 100% |
| 7 | Ghysels, E (2016) Macroeconomics and the reality of mixed frequency data | 0.511 | 2 | 1 | 100% |
| 8 | Gianfreda, A., Ravazzolo, F., and Rossini, L (2022) Large Time-Varying Volatility Models for Electricity Prices self | 0.511 | 2 | 1 | 100% |
| 9 | Giannone, D., Reichlin, L., and Small, D (2008) Nowcasting: The real-time informational content of macroeconomic data | 0.511 | 2 | 1 | 100% |
| 10 | Weron, R (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions | 0.585 | 3 | 1 |
| 2 | What drives the European carbon market? Macroeconomic factors and forecasts | 0.405 | 1 | 1 |
| 3 | Modeling European Electricity Market Integration during turbulent times | 0.405 | 1 | 1 |
| 4 | 2512.16521 | 0.405 | 1 | 1 |