@article{bf74f2521bcb40a2aeed692debdecacb,
title = "Easy bootstrap-like estimation of asymptotic variances",
abstract = "The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the bootstrap can be very time-consuming. In a recent paper, Honor{\'e} and Hu (2017), we propose a “Poor (Wo)man's Bootstrap” based on one-dimensional estimators. In this paper, we propose a modified, simpler method and illustrate its potential for estimating asymptotic variances.",
keywords = "Bootstrap, Censored regression, Inference, Standard error, Two-step estimation",
author = "Honor{\'e}, {Bo E.} and Luojia Hu",
note = "Funding Information: This research was supported by the Gregory C. Chow Econometric Research Program at Princeton University and by the National Science Foundation . The opinions expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System. We have benefited from discussion with Rachel Anderson and Mark Watson and from helpful comments from the editor and a referee. The most recent version of this paper will be posted at http://www.princeton.edu/ honore/papers/EasyBootstrap.pdf . Publisher Copyright: {\textcopyright} 2018",
year = "2018",
month = oct,
doi = "10.1016/j.econlet.2018.07.002",
language = "English (US)",
volume = "171",
pages = "46--50",
journal = "Economics Letters",
issn = "0165-1765",
publisher = "Elsevier",
}