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How to Combine Cointegration Tests

Title: How to Combine Cointegration Tests
Authors: Christian Bayer; Christoph Hanck; Cointegration Tests; Jel Code C; Christian Bayer Y; Christoph Hanck Z; Università Commerciale; L. Bocconi
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://edocs.ub.unimaas.nl/loader/file.asp?id=1312.
Publication Year: 2008
Collection: CiteSeerX
Subject Terms: Cointegration; Meta Test; Multiple Testing JEL-Codes; C12; C22
Description: This paper suggests a combination procedure to exploit the imperfect correlation of cointegration tests to develop a more powerful meta test. To exemplify, we combine Engle and Granger (1987) and Johansen (1988) tests. Either of these underlying tests can be more powerful than the other one depending on the nature of the data-generating process. The new meta test is at least as powerful as the more powerful one of the underlying tests irrespective of the very nature of the data generating process. At the same time, our new meta test avoids the arbitrary decision which test to use if single test results con‡ict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set. We apply our test to 143 data sets from published cointegration studies. There, in one third of all cases single tests give con‡icting results whereas our meta tests provides an unambiguous test decision.
Document Type: text
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.1266; http://edocs.ub.unimaas.nl/loader/file.asp?id=1312
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.1266; http://edocs.ub.unimaas.nl/loader/file.asp?id=1312
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.14224420
Database: BASE