Dynamic Asymmetric Causality Tests with an Application

TL;DR

This paper develops dynamic asymmetric causality tests using subsamples to better capture changing causal relationships in time-series data, with an application to economic and financial news impacts.

Contribution

It extends static asymmetric causality tests by incorporating dynamics through subsampling, allowing for parameter changes over time.

Findings

Dynamic tests reveal changing causality patterns over time.

Application shows asymmetric impacts of economic news vary across periods.

Method improves detection of evolving causal relationships.

Abstract

Testing for causation, defined as the preceding impact of the past values of one variable on the current value of another one when all other pertinent information is accounted for, is increasingly utilized in empirical research of the time-series data in different scientific disciplines. A relatively recent extension of this approach has been allowing for potential asymmetric impacts since it is harmonious with the way reality operates in many cases according to Hatemi-J (2012). 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 there exists a number of reasons for changing the potential causal connection between variables across time. The current paper extends therefore the static asymmetric causality tests by making them dynamic via the usage of subsamples. An application is also provided consistent with measurable definitions of economic or financial bad as well as good news and their potential interaction across time.