The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's 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. The bootstrap is a practical technique that is ready for use in applications. This chapter explains and illustrates the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The chapter outlines the theory of the bootstrap, provides numerical illustrations of its performance, and gives simple instructions on how to implement the bootstrap in applications. The presentation is informal and expository. Its aim is to provide an intuitive understanding of how the bootstrap works and a feeling for its practical value in econometrics.