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Inference when a nuisance parameter is weakly identified under the null hypothesis [An article from: Economics Letters]
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Book Details
Author(s)
S. Anatolyev
Publisher
Elsevier
ISBN / ASIN
B000RQYKLC
ISBN-13
978B000RQYKL2
Marketplace
France 🇫🇷
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Description
This digital document is a journal article from Economics Letters, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
When a nuisance parameter is weakly identified under the null hypothesis, the usual asymptotic theory breaks down and standard tests may exhibit significant size distortions. We provide asymptotic approximations under a drifting parameter DGP for distributions of classical tests and of those designed for the case of complete nonidentification. Simulations with a simple SETAR model show that the usual asymptotic theory does fail, although actual sizes of the classical Likelihood Ratio test display surprising robustness to the degree of identification.
Description:
When a nuisance parameter is weakly identified under the null hypothesis, the usual asymptotic theory breaks down and standard tests may exhibit significant size distortions. We provide asymptotic approximations under a drifting parameter DGP for distributions of classical tests and of those designed for the case of complete nonidentification. Simulations with a simple SETAR model show that the usual asymptotic theory does fail, although actual sizes of the classical Likelihood Ratio test display surprising robustness to the degree of identification.
