Collaborative Research: Nonparametric Distributional and Quantile Methods in Econometrics

合作研究：计量经济学中的非参数分布和分位数方法

• 批准号: 1061841
• 负责人: Victor Chernozhukov
• 金额: $ 22.54万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1061841&HistoricalAwards=false
• 关键词: Collaborative; Research; Nonparametric; Distributional; Quantile

The project has the main purpose of developing flexible statistical methods to analyze the effects of economic factors on the distribution of outcomes of interest. More specifically, our objective is to develop nonparametric distributional and quantile methods to estimate these effects in nonseparable models using cross sectional and panel data. Nonseparable models are important in Economics because they do not restrict the relationship between observable and unobservable variables. For cross sectional data, we analyze the properties of quantile regression series estimators. For panel data, we consider identification and estimation of average effects, quantile effects, and derivatives of structural functions in models with unrestricted individual heterogeneity. These methods can be applied to policy analysis. In particular, we develop inference methods to answer policy questions in rich economic models that allow for multiple sources of individual heterogeneity. For example, we can use panel data to test the hypothesis that the declining union premium across the wage distribution found by Chamberlain (1994) is explained by skill differences )unobserved heterogeneity) among unionized workers.The project's duration is three years, and it is strictly focused on the following five parts:(1) Conditional quantile processes in large models (series, many regressors);(2) Average and quantile effects in nonseparable panel models;(3) Derivatives of structural functions in nonseparable panel models;(4) Local average and quantile treatment effects in nonseparable panel models;(5) Nonparametric policy analysis.The nonparametric methods proposed are similar to methods commonly used to analyze mean effects, and expected to be quickly adopted and routinely used for practitioners. They can be implemented using standard software. The inference methods for policy analysis are also expected to have a broad impact since this type of analysis is commonly used in labor economics and other fields. A final purpose of the project is to produce public software in R that implements all the methods developed.

该项目的主要目的是开发灵活的统计方法，以分析经济因素对利益结果分布的影响。更具体地说，我们的目标是开发非参数分布和分位数的方法来估计这些影响在不可分离的模型使用横截面和面板数据。不可分离模型在经济学中很重要，因为它们不限制可观察变量和不可观察变量之间的关系。对于横截面数据，我们分析了分位数回归序列估计的性质。对于面板数据，我们考虑识别和估计的平均效应，分位数效应，和衍生物的结构功能在模型中的不受限制的个人异质性。这些方法可以应用于政策分析。特别是，我们开发的推理方法来回答政策问题，在丰富的经济模型，允许多个来源的个人异质性。例如，我们可以使用面板数据来检验Chamberlain（1994）发现的工资分布中工会溢价下降的假设，即工会工人之间的技能差异（未观察到的异质性）可以解释。该项目为期三年，严格集中在以下五个部分：（1）大型模型中的条件分位数过程（2）不可分离面板模型中的平均和分位数效应;（3）不可分离面板模型中结构函数的导数;（4）不可分离面板模型中的局部平均和分位数处理效应;（5）非参数政策分析。所提出的非参数方法类似于通常用于分析平均效应的方法，并有望迅速被实践者采用和常规使用。它们可以使用标准软件实现。政策分析的推理方法也将产生广泛的影响，因为这种分析通常用于劳动经济学和其他领域。该项目的最终目的是用R语言制作公共软件，实现所有开发的方法。