Tests of noncausality under Markov assumptions for qualitative panel data
Abstract
For many years, social scientists have been interested in obtaining testable definitions of causality (Granger 1969, Sims 1972). Recent works include those of Chamberlain (1982) and Florens and Mouchart (1982). The present paper first clarifies the results of these latter papers by considering a unifying definition of noncausality. Then, log-likelihood ratio (LR) tests for noncausality are derived for qualitative panel data under the minimal assumption that one series is Markov. LR tests for the Markov property are also obtained. Both test statistics have closed forms. These tests thus provide a readily applicable procedure for testing noncausality on qualitative panel data. Finally, the tests are applied to French Business Survey data in order to test the hypothesis that price changes from period to period are strictly exogenous to disequilibria appearing within periods.
Additional Information
© 1986 The Econometric Society. This research was done while the third author was visiting the Universite des Sciences Sociales de Toulouse. Support from DGRST 81-E-1303 is gratefully acknowledged. We are indebted to Jeff Dubin, Dave Grether, Jerry Kramer, Doug Rivers, Howard Rosenthal, two anonymous referees and the editor for helpful criticism. Ken McCue also provided computational help. Formerly SSWP 501.Attached Files
Published - sswp501_-_published.pdf
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Additional details
- Eprint ID
- 83201
- Resolver ID
- CaltechAUTHORS:20171114-150251205
- Direction Générale de la Recherche Scientifique et Technique
- DGRST 81-E-1303
- Created
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2017-11-15Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field