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dc.contributor.authorMiller, J. Isaaceng
dc.date.issued2010eng
dc.descriptionReproduced with permission from the Journal of Time Series Econometrics. doi: 10.2202/1941-1928.1057eng
dc.description.abstractA test for the rank of a vector error correction model (VECM) or panel VECM based on the well-known trace test is proposed. The proposed test employs instrumental variables (IV's)generated by a class of nonlinear functions of the estimated stochastic trends of the VECM under the null. The test improves on the standard trace test by replacing the non-standard critical values with chi-squared critical values. Extending the result to the panel VECM case, the test is robust to cross-sectional correlation of the disturbances. The nonlinear IV rank test also extends earlier research on nonlinear IV unit root tests. However, the optimal instrument in the univariate case is not admissible in the more general multivariate case. The chi-squared result suggests that IV tests may be used to replace limits of other standard tests with integrated time series that are given by nonstandard stochastic integrals, even without a panel with which to pool test statistics.eng
dc.identifier.citationMiller, J. Isaac (2010) "A Nonlinear IV Likelihood-Based Rank Test for Multivariate Time Series and Long Panels," Journal of Time Series Econometrics: Vol. 2: Iss. 1, Article 5.eng
dc.identifier.otherDOI: 10.2202/1941-1928.1057eng
dc.identifier.urihttp://hdl.handle.net/10355/10313eng
dc.languageEnglisheng
dc.publisherBerkeley Electronic Presseng
dc.relation.ispartofEconomics publications (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Economicseng
dc.relation.ispartofseriesWorking papers (Department of Economics);WP 10-01eng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subjectnonlinear instrumentseng
dc.subjectcointegrating rankeng
dc.subject.lcshEconometricseng
dc.titleA Nonlinear IV Likelihood-Based Rank Test for Multivariate Time Series and Long Panelseng
dc.typeArticleeng


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