Improved Lagrange multiplier tests in spatial autoregressions
针对空间自回归模型中的相关性检验，改进了拉格朗日乘子检验的小样本表现，基于Edgeworth展开和自助法提出新检验，蒙特卡洛模拟显示其优于传统卡方检验。
For testing lack of correlation against spatial autoregressive alternatives, Lagrange multiplier tests enjoy their usual computational advantages, but the (χ-super-2) first‐order asymptotic approximation to critical values can be poor in small samples. We develop refined tests for lack of spatial error correlation in regressions, based on Edgeworth expansion. In Monte Carlo simulations, these tests, and bootstrap tests, generally significantly outperform χ-super-2‐based tests.