Jan Ditzen, Erkal Ersoy, Haoyang Li, Francesco Ravazzolo
arXiv 2 Feb 2026 · Econometrics
arXiv:2602.01963 · PDF · DOI · OpenAlex · Extracted main text
This paper studies whether a small set of dominant countries can account for most of the dynamics of regional oil demand and improve forecasting performance. We focus on dominant drivers within the OECD and a broad GVAR sample covering over 90% of world GDP. Our approach identifies dominant drivers from a high-dimensional concentration matrix estimated row by row using two complementary variable-selection methods, LASSO and the one-covariate-at-a-time multiple testing (OCMT) procedure. Dominant countries are selected by ordering the columns of the concentration matrix by their norms and applying a criterion based on consecutive norm ratios, combined with economically motivated restrictions to rule out pseudo-dominance. The United States emerges as a global dominant driver, while France and Japan act as robust regional hubs representing European and Asian components, respectively. Including these dominant drivers as regressors for all countries yields statistically significant forecast gains over autoregressive benchmarks and country-specific LASSO models, particularly during periods of heightened global volatility. The proposed framework is flexible and can be applied to other macroeconomic and energy variables with network structure or spatial dependence.
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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 | Brownlees, Christian and Mesters, Geert (2021) Detecting granular time series in large panels | 1.000 | 9 | 3 | 100% |
| 2 | Sulaimanov, Nurgazy and Koeppl, Heinz (2016) Graph reconstruction using covariance-based methods | 0.874 | 5 | 2 | 100% |
| 3 | Ahrens, Achim and Hansen, Christian B. and Schaffer, Mark E (2020) lassopack: Model selection and prediction with regularized regression in Stata | 0.737 | 3 | 2 | 100% |
| 4 | Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian an… (2016) Inference in High-Dimensional Panel Models With an Application to Gun Control | 0.737 | 3 | 2 | 100% |
| 5 | Bickel, Peter J. and Ritov, Ya'acov and Tsybakov, Alexandre B (2009) Simultaneous analysis of lasso and dantzig selector | 0.737 | 3 | 2 | 100% |
| 6 | Kapetanios, G. and Pesaran, M. H. and Reese, S (2021) Detection of units with pervasive effects in large panel data models | 0.644 | 4 | 1 | 100% |
| 7 | Chernozhukov, Victor and Härdle, Wolfgang K. and Huang, Chen and Wan… (2019) LASSO-driven inference in time and space | 0.644 | 2 | 2 | 100% |
| 8 | Ahn, Seung C and Horenstein, Alex R (2013) Eigenvalue ratio test for the number of factors | 0.644 | 2 | 2 | 100% |
| 9 | Chudik, Alexander and Kapetanios, George and Pesaran, M Hashem (2018) A one covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models | 0.644 | 2 | 2 | 100% |
| 10 | Ditzen, Jan and Ravazzolo, Francesco (2022) Dominant Drivers of National Inflation self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 33 scored citations.