Standard Error Biases When Using Generated Regressors in Accounting Research
分析了会计研究中因使用生成回归量（如预测值、系数估计等）导致标准误被低估的问题，并通过模拟展示了偏差大小与生成回归量精度的反向关系，最后讨论了自助法作为修正工具。
ABSTRACT We analyze the standard error bias associated with the use of generated regressors—independent variables generated from first‐step regressions—in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the precision of the generated regressor. Finally, we discuss bootstrapping as a correction for the bias and demonstrate the pairs cluster bootstrap as a tool to improve inferences in common accounting settings involving generated regressors.