Semiparametric methods of policy analysis with social and economic network data

利用社会和经济网络数据进行政策分析的半参数方法

• 批准号: 1851647
• 负责人: Bryan Graham
• 金额: $ 27.25万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1851647&HistoricalAwards=false
• 关键词: Semiparametric; methods; policy; analysis; social

Relationships between economic agents are everywhere; firms source inputs from and provide outputs to other firms; nations negotiate and ratify treaties with each other; individuals depend upon networks of friends and acquaintances for fun, emotional support, information and advice. Despite this, rigorous methods for analyzing network data are not widely available. Existing methods are either geared toward descriptive analysis or make restrictive assumptions and are difficult to implement. The proposed research will develop: (i) new methods for policy analysis with two-way interaction outcome data and (ii) models of network formation with many and different agents. Frequently analyzed two-way interaction include trade and migration flows across countries, the value of input flows across firms, and friendships. The proposed methods could lead to efficient analyses and inference about several policy questions such as whether preferential trade agreements increase trade or whether democracy reduces inter-state warfare. The research will therefore aid in formulating efficient policies to govern interactions among individuals, groups, and nations. This will increase trade and exchange and thus improve economic efficiency in the US and around the world. The proposed research will develop: (i) nonparametric methods for policy analysis with dyadic outcome data and (ii) semiparametric models of network formation with heterogenous agents. The research will develop a uniform consistency results for a nonparametric dyadic regression estimator, formulate assumptions supporting causal inference using dyadic data (including estimation methods for proposed parameters of interest and their semiparametric efficiency bound analysis), and introduce methods of (efficient) semi- and non-parametric estimation. These methods are vast improvements over and generalizations of existing methods of network estimation. The research will also develop computation methods and provide ways to implement these new analytical methods. The PIs will make available software implementation of the proposed estimation and inference procedures in the form of a Python 3.6 package free of charge on PyPi and Github. In addition, all replication data, computer codes and supplemental research materials will also be made available online to support additional basic research, and to increase take-up of the proposed methods by policy analysts and empirical researchers. The development of these methods will improve analytical methods for dealing with network data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

经济主体之间的关系无处不在；公司从其他公司获得投入并向其他公司提供产出；国家之间相互谈判并批准条约；个人依靠朋友和熟人网络获得乐趣、情感支持、信息和建议。尽管如此，用于分析网络数据的严格方法并不广泛使用。现有的方法要么倾向于描述性分析，要么做出限制性的假设，很难实现。拟议的研究将发展：(I)利用双向互动结果数据进行政策分析的新方法，以及(Ii)具有多个不同代理人的网络形成模型。经常被分析的双向互动包括跨国贸易和移民流动、跨公司投入流动的价值以及友谊。拟议的方法可能导致对几个政策问题进行有效的分析和推断，例如优惠贸易协定是否增加了贸易，民主是否减少了国家间的战争。因此，这项研究将有助于制定有效的政策来管理个人、群体和国家之间的互动。这将增加贸易和交流，从而提高美国和世界各地的经济效率。建议的研究将发展：(I)具有二元结果数据的政策分析的非参数方法和(Ii)具有异质代理的网络形成的半参数模型。该研究将发展非参数并元回归估计的一致相合性结果，提出支持使用并元数据进行因果推断的假设(包括所提出的感兴趣参数的估计方法及其半参数有效界分析)，并介绍(有效)半参数估计和非参数估计的方法。这些方法是对现有网络估计方法的巨大改进和推广。这项研究还将开发计算方法，并提供实施这些新分析方法的途径。私人投资促进机构将在PYPI和Github上以PYTHON 3.6程序包的形式免费提供拟议估算和推断程序的软件实施。此外，还将在网上提供所有复制数据、计算机代码和补充研究材料，以支持更多的基础研究，并使政策分析员和实证研究人员更多地采用拟议的方法。这些方法的发展将改进处理网络数据的分析方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。