论文标题
预测回归的估计量：对金融经济学的可靠推断
An estimator for predictive regression: reliable inference for financial economics
论文作者
论文摘要
在金融经济学以及许多社会科学和其他地方，使用最小二乘和报告强大的标准错误估算线性回归非常普遍。对于在异性恋性下厚的尾部预测因子，这种推理的秘诀性能差，有时如此。在这里，我们开发了一种替代方法，只要结果和预测因子是有限的，就可以提供公正，一致和渐进的正常估计器。新方法在异性恋性下具有标准误差，易于可靠地估计和接近其标称大小的测试。该过程在模拟和经验练习中效果很好。分位数回归给出了扩展。
Estimating linear regression using least squares and reporting robust standard errors is very common in financial economics, and indeed, much of the social sciences and elsewhere. For thick tailed predictors under heteroskedasticity this recipe for inference performs poorly, sometimes dramatically so. Here, we develop an alternative approach which delivers an unbiased, consistent and asymptotically normal estimator so long as the means of the outcome and predictors are finite. The new method has standard errors under heteroskedasticity which are easy to reliably estimate and tests which are close to their nominal size. The procedure works well in simulations and in an empirical exercise. An extension is given to quantile regression.
