Zhan Gao, Zhentao Shi
arXiv 27 Jun 2018 · Statistics — Computation · publishedComputational Economics (2020)
arXiv:1806.10423 · PDF · DOI · OpenAlex · Extracted main text
Economists specify high-dimensional models to address heterogeneity in empirical studies with complex big data. Estimation of these models calls for optimization techniques to handle a large number of parameters. Convex problems can be effectively executed in modern statistical programming languages. We complement Koenker and Mizera (2014)'s work on numerical implementation of convex optimization, with focus on high-dimensional econometric estimators. Combining R and the convex solver MOSEK achieves faster speed and equivalent accuracy, demonstrated by examples from Su, Shi, and Phillips (2016) and Shi (2016). Robust performance of convex optimization is witnessed cross platforms. The convenience and reliability of convex optimization in R make it easy to turn new ideas into prototypes.
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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 | Shi, Z (2016) Econometric estimation with high-dimensional moment equalities self | 0.941 | 6 | 3 | 83% |
| 2 | Su, L., Z. Shi, and P. C. Phillips (2016) Identifying latent structures in panel data | 0.928 | 10 | 4 | 80% |
| 3 | Chen, W., X. Chen, C.-T. Hsieh, and Z. Song (2019) A forensic examination of china's national accounts | 0.874 | 9 | 2 | 100% |
| 4 | Koenker, R. and I. Mizera (2014) Convex optimization in R | 0.811 | 4 | 2 | 100% |
| 5 | Fu, A., N. Balasubramanian, and S. Boyd (2019) CVXR: An R package for disciplined convex optimization | 0.511 | 2 | 1 | 100% |
| 6 | Domahidi, A., E. Chu, and S. Boyd (2013) ECOS: An SOCP solver for embedded systems | 0.405 | 1 | 1 | 100% |
| 7 | Bajari, P., D. Nekipelov, S. P. Ryan, and M. Yang (2015) Machine learning methods for demand estimation | 0.405 | 1 | 1 | 100% |
| 8 | Bonhomme, S. and E. Manresa (2015) Grouped patterns of heterogeneity in panel data | 0.405 | 1 | 1 | 100% |
| 9 | Grant, M. and S. Boyd (2014) CVX: Matlab software for disciplined convex programming, version 2.1 | 0.405 | 1 | 1 | 100% |
| 10 | Diamond, S. and S. Boyd (2016) CVXPY: A Python-embedded modeling language for convex optimization | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | $_2$-Relaxation: With Applications to Forecast Combination and Portfolio Analysis | 0.405 | 1 | 1 |
| 2 | Spectral and Post-Spectral Estimators for Grouped Panel Data Models | 0.405 | 1 | 1 |