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 is 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 is frequently used. We use our methodology to re-assess Oneal and Russett's (...

Green, Yoon and Kim (hereinafter GYK) contribute to the literature on estimating pooled times-series--cross-section (hereinafter TSCS) models in International Relations (hereinafter IR). They argue that such models should be estimated with fixed effects when such effects are statistically necessary. While we obviously have no disagreement that sometimes fixed effects are appropriate, we show in this response that fixed effects are pernicious for IR TSCS models with a binary dependent variable (hereinafter BTSCS models) and that they are often problematic for IR models with a continuous dependent variable. In the binary case, this perniciousness is due to many pairs of nations always being scored zero, and hence having no impact on the parameter estimates; for example, many dyads never come into conflict. In the continuous case, fixed effects are problematic in the presence of the temporal stable regressors that are common IR applications, such as the dy...

Scholars in international relations (IR) are increasingly using time-series cross-section data to analyze models with a binary dependent variable (BTSCS models). IR scholars generally employ a simple logit/probit to analyze such data. This procedure is inappropriate if the data exhibit temporal or spatial dependence. First, we discuss two estimation methods for modelling temporal dependence in BTSCS data: one promising approach is based on exact modelling of the underlying temporal process which determines the latent, continuous, dependent variable; The other, and easier to implement, depends on the formal equivalence of BTSCS and discrete duration data. Because the logit estimates a discrete hazard in a duration context, this method adds a smoothed time term to the logit estimation. Second, we discuss spatial or cross-sectional issues, including robust standard errors and the modelling of effects. While it is not possible to use fixed effects in binary dependent variable panel models, such a strategy is feasible for IR BTSCS models. While not providing a model of spatial dependence, Huber's robust standard errors may well provide more accurate indications of parameter variability if the unit observations are intra-related. We apply these recommended techniques to reanalyses of the relationship between (1) democracy, interdependence and peace (Oneal, Oneal, Maoz and Russett); and (2) security and the termination of interstate rivalry (Bennett). The techniques appear to perform well statistically. Substantively, while democratic dyads do appear to be more peaceful, trade relations, as measured by Oneal, et al., do not decrease the likelihood of particpation in militarized disputes. Bennett's principal finding regarding security and rivalry termination is confirmed; his f...