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Tests of Noncausality under Markov Assumptions for Qualitative Panel Data
M. B. Bouissou
J. J. Laffont
Q. H. Vuong
Abstract
For many years, social scientists have been interested in obtaining testable definitions of causality (Granger [12], Sims [28]). Recent works include those of Chamberlain [7] and Florens and Mouchart [8]. 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.
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