The Annals of Statistics

On Bootstrapping Two-Stage Least-Squares Estimates in Stationary Linear Models

D. Freedman

Full-text: Open access

Abstract

For models similar to those used in econometric work, under suitable regularity conditions, the bootstrap is shown to give asymptotically valid approximations to the distribution of errors in coefficient estimates.

Article information

Source
Ann. Statist., Volume 12, Number 3 (1984), 827-842.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346705

Digital Object Identifier
doi:10.1214/aos/1176346705

Mathematical Reviews number (MathSciNet)
MR751275

Zentralblatt MATH identifier
0542.62051

JSTOR
links.jstor.org

Subjects
Primary: 62J05: Linear regression
Secondary: 62E20: Asymptotic distribution theory

Keywords
Regression standard errors two-stage least squares bootstrap linear models

Citation

Freedman, D. On Bootstrapping Two-Stage Least-Squares Estimates in Stationary Linear Models. Ann. Statist. 12 (1984), no. 3, 827--842. doi:10.1214/aos/1176346705. https://projecteuclid.org/euclid.aos/1176346705


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