The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests
介绍自助法和多重插补两种技术，它们通过重复抽样提高置信区间和临界值的准确性，并借助现代计算能力在计量经济学中广泛应用。
The bootstrap and multiple imputations are two techniques that can enhance the accuracy of estimated confidence bands and critical values. Although they are computationally intensive, relying on repeated sampling from empirical data sets and associated estimates, modern computing power enables their application in a wide and growing number of econometric settings. We provide an intuitive overview of how to apply these techniques, referring to existing theoretical literature and various applied examples to illustrate both their possibilities and their pitfalls.