Many political science research articles that use limited dependent variable models report estimated quantities, in particular, fitted probabilities, predicted probabilities, and functions of such probabilities, without indicating that such estimates are subject to uncertainty. This practice, along with the reporting of "percentage correctly predicted," can overstate the precision of reported results. In light of this, the present article describes a variety of measures of uncertainty that authors can include alongside estimates generated by limited dependent variable models. It also proposes an alternative to "percentage correctly predicted" and illustrates its calculations with congressional cosponsorship data from Krehbiel (1995)
Political scientists commonly focus on quantities of interest computed from model coefficients rathe...
2 ABSTRACT: A large number of different Pseudo-R measures for some common limited dependent variable...
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and es...
Many political science research articles that use limited dependent variable models re-port estimate...
Models designed for limited dependent variables are increasingly common in political science. Resea...
Research in political science is often concerned with modeling dependent variables that are proporti...
Research in political science is often concerned with modeling dependent variables that are proporti...
When facing small numbers of observations or rare events, political scientists often encounter separ...
Questions of causation are important issues in empirical research on political behavior. Most of the...
A large number of different Pseudo-R"2 measures for some common limited dependent variable mode...
We argue that political scientists can provide additional evidence for the predictive validity of ob...
Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of...
Applied economists have long struggled with the question of how to accommodate binary endogenous reg...
Methodologists and econometricians advocate the partial observability model as a tool that enables r...
Researchers in political science often estimate linear models of continuous outcomes using least squ...
Political scientists commonly focus on quantities of interest computed from model coefficients rathe...
2 ABSTRACT: A large number of different Pseudo-R measures for some common limited dependent variable...
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and es...
Many political science research articles that use limited dependent variable models re-port estimate...
Models designed for limited dependent variables are increasingly common in political science. Resea...
Research in political science is often concerned with modeling dependent variables that are proporti...
Research in political science is often concerned with modeling dependent variables that are proporti...
When facing small numbers of observations or rare events, political scientists often encounter separ...
Questions of causation are important issues in empirical research on political behavior. Most of the...
A large number of different Pseudo-R"2 measures for some common limited dependent variable mode...
We argue that political scientists can provide additional evidence for the predictive validity of ob...
Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of...
Applied economists have long struggled with the question of how to accommodate binary endogenous reg...
Methodologists and econometricians advocate the partial observability model as a tool that enables r...
Researchers in political science often estimate linear models of continuous outcomes using least squ...
Political scientists commonly focus on quantities of interest computed from model coefficients rathe...
2 ABSTRACT: A large number of different Pseudo-R measures for some common limited dependent variable...
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and es...