Semiparametric methods of policy analysis with social and economic network data

经济主体之间的关系无处不在；企业从其他企业获取投入并向其提供产出；各国之间相互谈判和批准条约；个人依赖于朋友和熟人的网络来获得乐趣、情感支持、信息和建议。尽管如此，分析网络数据的严格方法并没有广泛使用。现有的方法要么倾向于描述性分析，要么做出限制性假设，难以实施。拟议的研究将发展：(i)使用双向互动结果数据进行政策分析的新方法和（ii）由许多不同的代理人组成的网络形成模型。经常被分析的双向互动包括国家之间的贸易和移民流动、企业之间的投入价值流动以及友谊。所提出的方法可能导致对若干政策问题进行有效的分析和推断，例如优惠贸易协定是否会增加贸易或民主是否会减少国家间的战争。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。拟议的研究将发展：(i)使用二元结果数据进行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。本研究将为非参数并矢回归估计量开发一个一致的一致性结果，利用并矢数据制定支持因果推理的假设（包括对拟感兴趣的参数的估计方法及其半参数效率界分析），并介绍（有效的）半参数和非参数估计方法。这些方法是对现有网络估计方法的巨大改进和推广。研究还将开发计算方法，并提供实现这些新的分析方法的方法。pi将在PyPi和Github上以Python 3.6包的形式免费提供所建议的估计和推理过程的软件实现。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，以支持进一步的基础研究，并使政策分析人员和经验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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.