Revisiting Error‐Autocorrelation Correction: Common Factor Restrictions and Granger Non‐Causality*
质疑线性回归中“误差自相关修正”做法的合理性，证明采用AR(1)误差等价于假设被解释变量不格兰杰引起任何解释变量，并基于此构建公因子约束的新检验，通过蒙特卡洛模拟揭示该做法导致推断不可靠的其他潜在来源。
Abstract The paper questions the appropriateness of the practice known as ‘error‐autocorrelation correcting’ in linear regression, by showing that adopting an AR(1) error formulation is equivalent to assuming that the regressand does not Granger cause any of the regressors. This result is used to construct a new test for the common factor restrictions, as well as investigate – using Monte Carlo simulations – other potential sources of unreliability of inference resulting from this practice. The main conclusion is that when the Granger cause restriction is false, the ordinary least square and generalized least square estimators are biased and inconsistent, and using autocorrelation‐consistent standard errors does not improve the reliability of inference.