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Subsampling tests of parameter hypotheses and overidentifying restrictions with possible failure of identification


Wolf, Michael (2013). Subsampling tests of parameter hypotheses and overidentifying restrictions with possible failure of identification. International Journal of Approximate Reasoning, 54(6):769-792.

Abstract

We introduce a general testing procedure inmodels with possible identification failure that has exactasymptotic rejection probability under the null hypothesis. The procedure iswidely applicable and in this paper we apply it to tests of arbitrary linear parameter hypotheses as well as to tests of overidentification in time series models given by unconditional moment conditions. The main idea is to subsample classical tests, like for example theWald or the J test.More precisely, instead of using critical values based on asymptotic theory,we compute data-dependent critical values based on the subsampling technique.
We show that under full identification the resulting tests are consistent against fixed alternatives and that they have exact asymptotic rejection probabilities under the null hypothesis independent of identification failure. Furthermore, the subsampling tests of parameter hypotheses are shown to have the same local power as the original tests under full identification. An algorithm is provided that automates the block size choice needed to implement the subsampling testing procedure. A Monte Carlo study shows that the tests have reasonable size properties and often outperform other robust tests in terms of power.

Abstract

We introduce a general testing procedure inmodels with possible identification failure that has exactasymptotic rejection probability under the null hypothesis. The procedure iswidely applicable and in this paper we apply it to tests of arbitrary linear parameter hypotheses as well as to tests of overidentification in time series models given by unconditional moment conditions. The main idea is to subsample classical tests, like for example theWald or the J test.More precisely, instead of using critical values based on asymptotic theory,we compute data-dependent critical values based on the subsampling technique.
We show that under full identification the resulting tests are consistent against fixed alternatives and that they have exact asymptotic rejection probabilities under the null hypothesis independent of identification failure. Furthermore, the subsampling tests of parameter hypotheses are shown to have the same local power as the original tests under full identification. An algorithm is provided that automates the block size choice needed to implement the subsampling testing procedure. A Monte Carlo study shows that the tests have reasonable size properties and often outperform other robust tests in terms of power.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Language:English
Date:2013
Deposited On:23 May 2013 14:29
Last Modified:07 Dec 2017 21:14
Publisher:Elsevier Inc.
ISSN:0888-613X
Additional Information:this is the author’s version of a work that was accepted for publication in <Journal title>. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Approximate Reasoning, [VOL 54, ISSUE 9, (2013)] DOI: 10.1016/j.ijar.2012.12.003
Publisher DOI:https://doi.org/10.1016/j.ijar.2012.12.003

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