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摘要翻译：
经济学家指定高维模型来解决复杂大数据的实证研究中的异质性。这些模型的估计需要优化技术来处理大量的参数。凸问题可以在现代统计程序设计语言中有效地执行。我们补充了Koenker和Mizera(2014)关于凸优化的数值实现的工作，重点是高维计量经济估计量。Su、Shi和Phillips(2016)和Shi(2016)的例子表明，结合R和凸解算器MOSEK实现了更快的速度和等效的精度。凸优化算法的鲁棒性能是跨平台的。凸优化在R中的方便性和可靠性使得将新想法转化为原型变得容易。
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英文标题：
《Implementing Convex Optimization in R: Two Econometric Examples》
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作者：
Zhan Gao, Zhentao Shi
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最新提交年份：
2019
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分类信息：
一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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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PDF链接：
https://arxiv.org/pdf/1806.10423
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