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J检验向空间面板数据框架的扩展AI 解读

Summary Kelejian ( Letters in Spatial and Resources Sciences ; 1 : 3–11) extended the J ‐test procedure to a spatial framework. Although his suggested test was computationally simple and intuitive, it did not use the available information in an efficient manner. Kelejian and Piras ( Regional Science and Urban Economics ; 41 : 281–292) generalized and modified Kelejian's test to account for all the available information. However, neither Kelejian ( ) nor Kelejian & Piras ( )…

含自回归与异方差扰动的空间自回归模型：设定与估计AI 解读

This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in (Kelejian and Prucha, 1998) and (Kelejian and Prucha, 1999) for the spatial autoregressive parameter in the disturbance process. We also define IV estimators for the…

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联立方程估计量的结构：向非正态扰动的推广AI 解读

A general linear simultaneous equation system with a multivariate Student t disturbance vector is considered. The normal equations of the corresponding maximum likelihood estimator are used as estimator generating equations to introduce a new class of estimators. Properties of large subclasses of these estimators are determined for disturbance vectors other than the multivariate Student t.

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非均衡市场的估计方法：进一步研究AI 解读

This paper is concerned with the problem of estimating demand and supply schedules in disequilibrium markets. The results of Fair and Jaffee are expanded in three ways. (1) Their directional method I is modified to yield consistent estimates. (2) A maximum likelihood alternative to their quantitative method is proposed. (3) The price equation is generalized to be a multivariate, stochastic function, and a method is proposed for estimating demand and supply schedules in this…

参数线性但内生变量非线性的计量系统与两阶段最小二乘AI 解读

It is demonstrated that a variant of the two-stage least squares technique can be used to estimate the parameters of a nonlinear model. To do this, the reduced form equations of such models are derived and discussed; then certain problems particular to the estimation of nonlinear models are considered.