Implementing the Single Bootstrap: Some Computational Considerations

B McCullough and Hrishikesh Vinod

Computational Economics, 1993, vol. 6, issue 1, 15 pages

Abstract: In applied econometrics, the researcher typically has two recourses for conducting inference: assuming normal errors or relying on asymptotic theory. In economic models, the assumption of normal errors is rarely justified and, for moderate sample sizes, the applicability of a central limit theorem is questionable. Researchers now have a third alternative: the bootstrap. Central to the bootstrap methodology is the idea that computational force can substitute for theoretical analysis. This article explains the bootstrap method, shows how a simple transformation can improve the reliability of inference, gives an algorithm for bootstrapping a regression equation, and discusses some computational pitfalls. Citation Copyright 1993 by Kluwer Academic Publishers.

Date: 1993
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:6:y:1993:i:1:p:1-15

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().