Specification and Estimation of Econometric Models with Interactions

该项目的目标是改进具有相互作用的计量经济学模型，特别是空间自回归模型和社会相互作用模型。该项目将研究模型规格，估计，统计推断，以及可能的经验应用这些模型。 在同伴群体影响的实证研究中，检测社会互动的主要问题是将同伴效应与其他相关不可观察因素的混杂影响分开。 该项目的研究活动将包括在不可观测的情况下识别和估计各种相互作用效应的研究。 本计画研究空间模式的特徴，以辨识与评估同侪群体的互动。 空间自回归模型在研究社会相互作用中很有用。 这个项目开发了计算上易于处理的，以及有效的估计方法，这样的模型。将开发统计推断程序，以测试相关的不可观测值。 将开发区分具有相互作用的各种模型的方法。在大群体相互作用的情况下，各种相互作用效应的估计可能有不同的收敛速度。 在这种情况下，适当的统计推断需要调查。 本计画在最大似然与广义矩框架下，研究经典推论统计量的渐近性质。 还将采用最短距离法。对于博弈环境中的战略互动或价格竞争，空间自回归模型可以被视为反应函数。通常的规格包括对手行动的加权平均值。然而，在某些情况下，反应函数可能取决于结果的完全分布或其分布的一些其他特征，例如其极值，分位数或秩统计量。 本项目考虑了这种反应函数模型的规格和估计。社交交互模型还可以对涉及离散选择和/或样本选择的个体行为进行建模。 本计画针对有限因变数之交互作用模式，发展计算上容易处理且统计上有效的估计方法。正式的统计性质的估计将被研究。计划对拟议的模型和方法进行实证和蒙特卡罗研究。易处理的估计和统计推断程序的发展为广泛的重要经济问题的实证调查提供了统计工具。这些工具有助于了解经济的发展和个人的社会互动。适当的计量经济学模型和方法可以发现并准确衡量可能的空间或社会相互作用的影响。它们为评估教育政策和社会计划的有效性提供了改进的方法。它们还可能对企业管理、营销和规划具有商业价值。

The objective of this project is to improve econometric models with interactions, in particular, spatial autoregressive models and social interaction models. The project will investigate model specification, estimation, statistical inference, and possible empirical applications of such models. The main problem in detecting social interactions in empirical studies of peer group influence is to separate peer effects from other confounding influences of correlated unobservables. Research activity of this project will include the study of identification and estimation of various interaction effects in the presence of unobservables. This project investigates features of spatial models that allow identification and estimation of peer group interactions. Spatial autoregressive models are useful in the study of social interactions. This project develops computationally tractable as well as efficient estimation methods for such models. Statistical inference procedures will be developed to test correlated unobservables. Methods to discriminate among various models with interactions will be developed. In the scenario of large group interactions, estimates of various interaction effects may have different rates of convergence. Proper statistical inference under such situations needs to be investigated. This project considers the asymptotic properties of classical inference statistics in both the maximum likelihood and GMM framework. The minimum distance method will also be pursued. For strategic interactions or price competition in a game setting, the spatial autoregressive model can be regarded as a reaction function. The usual specification involves a weighted average of opponents' actions. In certain situations, a reaction function may, however, depend on the full distribution of outcomes or some other characteristics of its distribution, such as its extremum, quantile or rank statistics. This project considers the specification and estimation of such reaction function models. Social interactions models may also model individual behavior involving discrete choices and/or sample selection. This project develops computationally tractable and statistically efficient estimation methods for interactions models with limited dependent variables. Formal statistical properties of estimators will be studied. Empirical and Monte Carlo studies are planned for the proposed models and methodologies. The development of tractable estimation and statistical inference procedures provides statistical tools for empirical inquiries on a wide range of important economic issues. Those tools are useful for understanding the development of an economy and social interactions of individuals. Proper econometric models and methods can detect and provide accurate measurement on the impact of possible spatial or social interactions. They provide improved methods for evaluating the effectiveness of educational policies and social programs. They may also have commercial values for business administration, marketing and planning.