Is Double Trouble? – How to Combine Cointegration Tests
AbstractThis 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 conflict. 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 conflicting results whereas our meta test provides an unambiguous test decision.
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Bibliographic InfoPaper provided by Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen in its series Ruhr Economic Papers with number 0048.
Length: 27 pages
Date of creation: May 2008
Date of revision:
Other versions of this item:
- Bayer, Christian & Hanck, Christoph, 2008. "Is Double Trouble? How to Combine Cointegration Tests," Research Memoranda 014, Maastricht : METEOR, Maastricht Research School of Economics of Technology and Organization.
- Bayer, Christian & Hanck, Christoph, 2008. "Is double trouble? How to combine cointegration tests," Technical Reports 2008,10, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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