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空间计量模型贝叶斯估计的大样本性质AI 解读

This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial autoregressive Tobit model, as an example of nonlinear spatial models.…

动态网络形成与互动的空间建模方法AI 解读

This study primarily seeks to answer the following question: How do social networks evolve over time and affect individual economic activity? To provide an adequate empirical tool to answer this question, we propose a new modeling approach for longitudinal data of networks and activity outcomes. The key features of our model are the inclusion of dynamic effects and the use of time-varying latent variables to determine unobserved individual traits in network formation and…

含时变内生空间权重、共同因子与随机系数的空间面板自回归模型：贝叶斯分析AI 解读

This article examines spatial panel autoregressive (SAR) models with dynamic, time-varying endogenous spatial weights matrices, common factors, and random coefficients. An empirical application is on the spillover effects of state Medicaid spending. Endogeneity of spatial weights matrices comes from the correlation of “economic distance” and the disturbances in the SAR equation. Common factors control for common shocks to all states and random coefficients may capture…