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空间计量模型的最优线性与二次矩：美国县域就业增长应用AI 解读

Summary We provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross‐sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi‐maximum likelihood estimator when the disturbances follow a non‐normal and unknown distribution. We apply this procedure to a high‐order spatial autoregressive model with…

短时间维度下内生空间权重动态面板模型的工具变量估计AI 解读

The spatial dynamic panel data (SDPD) model is a standard tool for analysing data with both spatial correlation and dynamic dependences among economic units. Conventional estimation methods rely on the key assumption that the spatial weight matrix is exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. In this paper, we propose an SDPD model with individual fixed effects in a short time dimension,…

空间 Durbin 面板模型的识别AI 解读

Summary This paper considers identification of spatial Durbin dynamic panel models under 2SLS and ML estimations. We show that the parameters are generally identified via 2SLS moment relations or expected log‐likelihood or quasi‐likelihood functions. Monte Carlo experiments suggest that omitting relevant Durbin terms can result in significant biases in regression estimates, while including an irrelevant Durbin term causes no obvious loss of efficiency. Empirical illustration…

因变量随机缺失时空间自回归模型的估计AI 解读

We suggest and compare different methods for estimating spatial autoregressive models with randomly missing data in the dependent variable. Aside from the traditional expectation-maximization (EM) algorithm, a nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that generalized…

信号博弈实验中的同伴学习AI 解读

SUMMARY We investigate peer group effects in laboratory experiments based on Milgrom and Roberts' (1982, Econometrica 50 : 443–459) entry limit pricing game. We generalize Heckman's (1981, in Structural Analysis of Discrete Data with Econometric Applications . MIT Press: Cambridge, MA) dynamic discrete‐choice panel data models by introducing time‐lagged social interactions, using the unbiased GHK simulator to implement the computationally cumbersome maximum likelihood…