Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable
Researchers typically analyze time-series-cross-section data with a binary dependent variable (BTSCS) using ordinary logit or probit. However, BTSCS observations are likely to violate the independence assumption of the ordinary logit or probit statistical model. It is well known that if the observations are temporally related that the results of an ordinary logit or probit analysis may be misleading. In this paper, we provide a simple diagnostic for temporal dependence and a simple remedy. Our remedy is based on the idea that BTSCS data are identical to grouped duration data. This remedy does not require the BTSCS analyst to acquire any further methodological skills, and it can be easily implemented in any standard statistical software package. While our approach is suitable for any type of BTSCS data, we provide examples and applications from the field of International Relations, where BTSCS data are frequently used. We use our methodology to reassess Oneal and Russett's (1997) findings regarding the relationship between economic interdependence, democracy, and peace. Our analyses show that (1) their finding that economic interdependence is associated with peace is an artifact of their failure to account for temporal dependence yet (2) their finding that democracy inhibits conflict is upheld even taking duration dependence into account.