ABSTRACT

This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference that matches the ordinary least squares estimator of the two SAR coefficients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The resulting estimator is shown to be consistent, asymptotically normal and robust to unknown heteroscedasticity. Monte Carlo experiments are provided to show its finite-sample performance in comparison with existing estimators that are based on the generalized method of moments. The new estimation procedure is applied to empirical studies on teenage pregnancy rates and Airbnb accommodation prices.

摘要

本文为一阶空间自回归 (SAR) 模型提出了一种新的估计程序，其中干扰项也遵循一阶自回归，其创新可能是异方差的。估计程序基于间接推理的原理，将两个 SAR 系数（一个在结果方程中，另一个在干扰方程中）的普通最小二乘估计量与其近似分析期望相匹配。所得估计量被证明是一致的、渐近正态的并且对未知的异方差具有鲁棒性。提供 Monte Carlo 实验以显示与现有基于广义矩方法的估计器相比的有限样本性能。