Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations
研究发现似不相关回归模型中标准误存在严重向下偏误，而自助法应用于t统计量而非标准误时能显著改善推断效果，对计量经济学研究者有参考价值。
The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t -ratios rather than to standard errors.