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Below results based on the criteria 'temporal dependence'
Total number of records returned: 2
GEE Models of Judicial Behavior
generalized estimating equations
time-series cross-sectional data
judicial decision making
The assumption of independent observations in judicial decision making flies in the face of our theoretical understanding of the topic. In particular, two characteristics of judicial decision making on collegial courts introduce heterogeneity into successive decisions: individual variation in the extent to which different jurists maintain consistency in their voting behavior over time, and the ability of one judge or justice to influence another in their decisions. This paper addresses these issues by framing judicial behavior in a time-series cross-section context and using the recently developed technique of generalized estimating equations (GEE) to estimate models of that behavior. Because the GEE approach allows for flexible estimation of the conditional correlation matrix within cross-sectional observations, it permits the researcher to explicitly model interjustice influence or over-time dependence in judicial decisions. I utilize this approach to examine two issues in judicial decision making: latent interjustice influence in civil rights and liberties cases during the Burger Court, and temporal consistency in Supreme Court voting in habeas corpus decisions in the postwar era. GEE estimators are shown to be comparable to more conventional pooled and TSCS techniques in estimating variable effects, but have the additional benefit of providing empirical estimates of time- and panel- based heterogeneity in judicial behavior.
Beyond Ordinary Logit: Taking Time Seriously in Binary Time-Series--Cross-Section Models
binary time-series--cross-section data
grouped duration models
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 (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 and 2) their finding that democracy inhibits conflict is upheld even taking duration dependence into account.