FINITE-SAMPLE BIAS OF THE QMLE IN SPATIAL AUTOREGRESSIVE MODELS
研究了空间自回归模型中拟极大似然估计在有限样本下的偏误，推导了偏误的近似公式，并提出了可行的偏误校正方法，蒙特卡洛模拟显示该方法效果良好。
We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly applicable. In some special cases, the analytical bias result can be significantly simplified. Our Monte Carlo results demonstrate that the feasible bias-correction procedure works remarkably well.