Andrea Bastianin, Luca Rossini, Lorenzo Tonni
arXiv 18 Dec 2025 · Econometrics
arXiv:2512.16521 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a real-time forecasting framework for the monthly real prices of four key industrial metals -- aluminum, copper, nickel, and zinc -- whose demand is rising due to their widespread use in manufacturing and low-carbon technologies. To replicate the information set available to forecasters in real time, we construct a new dataset combining daily financial variables with first-release macroeconomic indicators and use nowcasting techniques to address publication lags. Within this real-time environment, we evaluate the predictive accuracy of a broad set of univariate, multivariate, and factor-augmented models, comparing their performance with two industry benchmarks: survey expectations and futures-spot spread models. Results show that although short-run metal price movements remain difficult to predict, medium-term horizons display substantial forecastability. Indicators of manufacturing activity tied to primary metals -- such as new orders and capacity utilization -- significantly improve forecasting accuracy for aluminum and copper, with more moderate gains for zinc and limited improvements for nickel. Futures and survey forecasts generally underperform the real-time econometric models. These findings highlight the value of incorporating timely macroeconomic information into forecasting frameworks for industrial metal markets.
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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 | Diebold, Francis X and Mariano, Robert S (1995) Comparing predictive accuracy | 1.000 | 5 | 4 | 100% |
| 2 | Baumeister, Christiane and Huber, Florian and Lee, Thomas K. and Rav… (2025) Forecasting Natural Gas Prices in Real Time | 0.737 | 3 | 2 | 100% |
| 3 | Ruben, Aag and Bjørnland, Hilde C. and Eliassen, Peder (2025) Forecasting Oil and Natural Gas Prices: A Model Combination Approach | 0.644 | 2 | 2 | 100% |
| 4 | Hansen, Peter R and Lunde, Asger and Nason, James M (2011) The model confidence set | 0.644 | 2 | 2 | 100% |
| 5 | Alquist, Ron and Kilian, Lutz (2010) What do we learn from the price of crude oil futures? | 0.511 | 2 | 1 | 100% |
| 6 | Stock, James H and Watson, Mark W (2002) Forecasting using principal components from a large number of predictors | 0.511 | 2 | 1 | 100% |
| 7 | Andrea Bastianin and Chiara Casoli and Marzio Galeotti (2023) The connectedness of Energy Transition Metals self | 0.405 | 1 | 1 | 100% |
| 8 | Christiane Baumeister and Guillermo Verduzco-Bustos and Franziska Oh… (2022) Pandemic, war, recession: Drivers of aluminum and copper prices | 0.405 | 1 | 1 | 100% |
| 9 | Jennifer Considine and Philipp Galkin and Emre Hatipoglu and Abdulla… (2023) The effects of a shock to critical minerals prices on the world oil price and inflation | 0.405 | 1 | 1 | 100% |
| 10 | Carriero, Andrea and Clark, Todd E. and Marcellino, Massimiliano (2015) Bayesian VARs: Specification Choices and Forecast Accuracy | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quantifying Demand Shocks in the Green and Digital Transition | 0.405 | 1 | 1 |