Estimation of Spatial Autoregressive Econometric Models with Continous and Limited Dependent Variables

具有连续和有限因变量的空间自回归计量经济学模型的估计

• 批准号: 0111380
• 负责人: Lung-fei Lee
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-15 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0111380&HistoricalAwards=false
• 关键词: Estimation; Spatial; Autoregressive; Econometric; Models

The field of spatial econometrics is concerned with the use of statistical and econometric techniques to handle spatial effects in multiregional economic models and economic interaction of agents located in space. Proper spatial econometric models need to be developed to empirically validate modern spatial economic theories. Further empirical developments are motivated by the growing importance of Geographic Information Systems (GIS) in regional and urban policy analyses. This project develops econometric methodologies for the estimation and testing of complex spatial econometric models including spatial models with limited dependent variables. This project proceeds in several directions. Statistical properties of some popular estimation methods in the literature are often assumed without detailed investigation into possibly distinctive features of a spatial econometric model. While some claims are correct under certain spatial scenarios, they might not be so in others. The existing literature on spatial econometrics has mainly focused on models with small group interaction but models with large group interaction have many interesting potential applications. This project continues the investigation of statistical properties of popular estimators for spatial autoregressive models with large group interaction. Estimators for spatial models with large group interaction have quite different statistical properties from those of models with small group interaction. This project explores alternative models to capture social interaction effects and addresses issues of random sample and possible incomplete spatial interactions in spatial models. In addition, statistical procedures are developed for distinguishing spatial models from social effect models.Spatial models with agents making interactive discrete choices are useful for research on innovation diffusion processes. The estimation of a discrete choice model with spatial interaction can be quite challenging as its likelihood function involves high dimensional integral. The dimension of integration can be as large as the number of sample observations. In order to develop these models to their full capacities, computationally tractable methods must be developed. This project develops estimation methods based on simulation estimation methodologies. The effectiveness of various simulation estimation methods, which include the method of simulated maximum likelihood, the method of simulated EM algorithm, the method of simulated scores, and the Gibbs sampler, are investigated. Statistical properties of those estimators are studied. In addition to estimation, test statistics for spatial correlations and diagnostics are also developed. This project develops computationally tractable generalized method of moments for the estimation of spatial autoregressive models of any finite order with or without the presence of exogenous variables. Asymptotic properties of such estimators are investigated.Dynamic discrete choice models capture various notions of dynamic effects, state dependence, heterogeneity, and spurious correlation in a panel data setting. This project generalizes existing models to incorporate possible contemporaneous and intertemporal spatial interaction effects. Spatial dynamics in discrete choices are of special interest. Special attention is paid to the specification and estimation of such panel data models. Even though the development of econometric methods is the main focus of this project, empirical studies with panel data from developing countries illustrate the useful of the new models and the feasibility of the methodologies.

空间计量经济学领域关注的是使用统计和计量经济学技术来处理多区域经济模型中的空间效应和空间主体的经济相互作用。需要开发适当的空间计量经济学模型，以实证验证现代空间经济理论。 地理信息系统（GIS）在区域和城市政策分析中的重要性日益增加，推动了进一步的实证发展。 该项目开发了用于估计和测试复杂空间计量经济模型的计量经济学方法，包括具有有限因变量的空间模型。该项目在几个方向上进行。 在文献中，一些流行的估计方法的统计特性往往是假设没有详细的调查可能独特的功能的空间计量经济模型。 虽然有些说法在某些空间情景下是正确的，但在其他情景下可能并非如此。现有的空间计量经济学文献主要集中在小群体相互作用的模型上，但大群体相互作用的模型有许多有趣的潜在应用。 本计画继续研究具有大群体交互作用的空间自回归模型常用估计量的统计性质。 具有大群体交互作用的空间模型的估计量与具有小群体交互作用的空间模型的估计量具有完全不同的统计性质。 这个项目探讨了替代模型来捕捉社会互动效应，并解决了空间模型中随机样本和可能不完整的空间互动问题。 此外，本文还提出了区分空间模型和社会效应模型的统计方法，指出了具有交互离散选择的空间模型对于创新扩散过程的研究是有用的。具有空间交互作用的离散选择模型的似然函数涉及高维积分，其估计具有相当大的挑战性。积分的维数可以与样本观测的数量一样大。 为了充分发挥这些模型的能力，必须开发计算上易于处理的方法。本项目开发基于模拟估算方法的估算方法。研究了模拟极大似然法、模拟EM算法、模拟得分法和Gibbs抽样法等模拟估计方法的有效性。 这些估计的统计性质进行了研究。除了估计，空间相关性和诊断的检验统计也被开发。 本计画发展计算上容易处理的广义矩量法，以估计任何有限阶空间自回归模型，不论是否有外生变数的存在。 动态离散选择模型在面板数据环境中捕捉动态效应、状态依赖、异质性和虚假相关的各种概念。 这个项目概括了现有的模型，将可能的同时期和跨时间的空间相互作用的影响。离散选择中的空间动力学特别令人感兴趣。 特别注意的是这种面板数据模型的规格和估计。尽管发展计量经济学方法是本项目的主要重点，但对发展中国家的面板数据进行的实证研究表明，新模式是有用的，方法是可行的。