期刊
JOURNAL OF ECONOMETRICS
卷 133, 期 2, 页码 443-477出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2005.06.007
关键词
Monte Carlo test; maximized Monte Carlo test; finite-sample test; exact test; nuisance parameter; bounds; bootstrap; parametric bootstrap; simulated annealing; asymptotics; nonstandard asymptotic distribution
The technique of Monte Carlo (MC) tests [Dwass (1957, Annals of Mathematical Statistics 28, 181-187); Barnard (1963, Journal of the Royal Statistical Society, Series B 25, 294)] provides a simple method for building exact tests from statistics whose finite sample distribution is intractable but can be simulated (when no nuisance parameter is involved). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing it to statistics whose null distribution involves nuisance parameters [maximized MC (MMC) tests]. Simplified asymptotically justified versions of the MMC method are also proposed: these provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics. (c) 2005 Elsevier B.V. All rights reserved.
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