Bootstrap Methods in Econometrics

TL;DR

The paper discusses the bootstrap method's effectiveness in econometrics for more accurate inference, highlighting its advantages, limitations, and practical applications in complex settings.

Contribution

It provides an accessible explanation of bootstrap methods' usefulness and limitations in econometrics, emphasizing their improved accuracy over traditional asymptotic approaches.

Findings

Bootstrap yields more accurate distribution approximations.

It improves coverage probabilities of confidence intervals.

It enhances hypothesis testing accuracy.

Abstract

The bootstrap is a method for estimating the distribution of an estimator or test statistic by re-sampling the data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in references that are cited.