摘要翻译：
结构估计是经验经济学中的一种重要方法，通过广义矩量法(GMM)估计了大量的结构模型。传统上，结构模型的选择是基于模型对估计的拟合来进行的，这种模型取的是整个观测样本。本文提出了一种基于交叉验证(CV)的模型选择方法，该方法利用样本分裂技术来避免过拟合等问题。虽然CV在机器学习领域得到了广泛的应用，但我们率先证明了它在GMM框架中模型选择的一致性。通过对IV回归模型和寡头市场模型的模拟，将其经验性质与现有方法进行了比较。此外，我们还提出了将我们的方法应用于平衡约束数学规划(MPEC)方法的方法。最后，我们将我们的方法应用于网上零售销售数据，将动态市场模型与静态市场模型进行比较。
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英文标题：
《Cross Validation Based Model Selection via Generalized Method of Moments》
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作者：
Junpei Komiyama and Hajime Shimao
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最新提交年份：
2018
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分类信息：

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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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  Structural estimation is an important methodology in empirical economics, and a large class of structural models are estimated through the generalized method of moments (GMM). Traditionally, selection of structural models has been performed based on model fit upon estimation, which take the entire observed samples. In this paper, we propose a model selection procedure based on cross-validation (CV), which utilizes sample-splitting technique to avoid issues such as over-fitting. While CV is widely used in machine learning communities, we are the first to prove its consistency in model selection in GMM framework. Its empirical property is compared to existing methods by simulations of IV regressions and oligopoly market model. In addition, we propose the way to apply our method to Mathematical Programming of Equilibrium Constraint (MPEC) approach. Finally, we perform our method to online-retail sales data to compare dynamic market model to static model.
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PDF链接：
https://arxiv.org/pdf/1807.06993