Inference on higher-order spatial autoregressive models with increasingly many parameters
研究了当模型阶数和回归元数量随样本量缓慢增加时，高阶空间自回归模型参数估计的一致性和渐近正态性，并分析了最小二乘和工具变量估计的适用条件。
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order spatial autoregressive model whose order, and number of regressors, are allowed to approach infinity slowly with sample size. Both least squares and instrumental variables estimates are examined, and the permissible rate of growth of the dimension of the parameter space relative to sample size is studied. Besides allowing the number of parameters to increase with the data, this has the advantage of accommodating some asymptotic regimes that are suggested by certain spatial settings, several of which are discussed. A small empirical example is also included, and a Monte Carlo study analyses various implications of the theory in finite samples.