The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests
AbstractThe 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.
CitationBrownstone, David, and Robert Valletta. 2001. "The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests." Journal of Economic Perspectives, 15 (4): 129-141. DOI: 10.1257/jep.15.4.129
- C20 Single Equation Models; Single Variables: General
- C13 Estimation
- C12 Hypothesis Testing