Dynamic Asymmetric Causality Tests with an Application †

Testing for causation—defined as the preceding impact of the past value(s) of one variable on the current value of another when all other pertinent information is accounted for—is increasingly utilized in empirical research using the time-series data in different scientific disciplines. A relatively recent extension of this approach has been allowing for potential asymmetric impacts, since this is harmonious with the way reality operates, in many cases. The current paper maintains that it is also important to account for the potential change in the parameters when asymmetric causation tests are conducted, as several reasons exist for changing the potential causal connection between the variables across time. Therefore, the current paper extends the static asymmetric causality tests by making them dynamic via the use of subsamples. An application is also provided consistent with measurable definitions of economic, or financial bad, as well as good, news and their potential causal interactions across time.