This paper studies estimation of a partially specified spatial autoregressive model with heteroskedasticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, the unknown parameter is estimated by applying the instrumental variable estimation. Under certain sufficient conditions, the proposed estimator for the finite dimensional parameters is shown to be root-n consistent and asymptotically normally distributed; The proposed estimator for the unknown function is shown to be consistent and asymptotically distributed as well, though at a rate slower than root-n. Consistent estimators for the asymptotic variance-covariance matrices of both estimators are provided. Monte Carlo simulations suggest that the proposed procedure has some practical value.
本文将研究贝叶斯法则视角下的空间自相关误差自相关模型(Spatial Autoregressive Model with Autoregressive Disturbances,SARAR模型)变量选择问题。通过将基于BIC准则的子集选择法推广到空间模型,实现SARAR模型的变量选择,并证明在一定条件下,对于SARAR模型的变量选择BIC准则具有良好的渐近性质。同时本文还将利用Monte Carlo模拟验证BIC准则能够很好的实现SARAR模型的变量选择。最后以股票收益率为例,在验证股票收益率具有空间效应的前提下,利用BIC准则对影响股票收益率的众多财务指标进行变量选择。
