Bootstrap inference in econometrics.
The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o...
| Published in: | Canadian Journal of Economics Vol. 35; no. 4; pp. 615 - 646 |
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| Format: | Article |
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Wiley-Blackwell
November 2002
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| Online Access: | View this record in EBSCOhost |
| Summary: | The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. I discuss Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case. Reprinted by permission of the publisher. |
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