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Below results based on the criteria 'Emulation'
Total number of records returned: 2
Potential Ambiguities in a Directed Dyad Approach to State Policy Emulation
In this paper I discuss circumstances under which the dyadic model of policy diffusion can produce misleading estimates in favor of policy emulation. These circumstances arise in the context of state pain management policy, and correspond generally to policies that states are uniformly expanding. When this happens, dyadic models of policy diffusion conflate policy emulation and policy adoption: since early adopters are policy leaders, later adopters will appear to emulate them, even if they are merely stragglers acting on their own accord. I demonstrate the possibility of this ambiguity analytically and through Monte Carlo simulation. Both start with the assumption that the data are generated according to a standard, monadic model of policy adoption and then converted to a dyadic model, which can incorrectly produce evidence of emulation. I propose a simple modification of the dyadic emulation model --- conditioning on the opportunity to emulate --- and show that it is much less likely to produce inaccurate findings. I then return to the study of pain management policy and find substantial differences between the dyadic emulation model and the conditional emulation model.
The Spatial Probit Model of Interdependent Binary Outcomes: Estimation, Interpretation, and Presentation
Bayesian Gibbs-Sampler Estimator
Recursive Importance-Sampling Estimator
We have argued and shown elsewhere the ubiquity and prominence of spatial interdependence in political science research and noted that much previous practice has neglected this interdependence or treated it solely as nuisance to the serious detriment of sound inference. Previously, we considered only linear-regression models of spatial and/or spatio-temporal interdependence. In this paper, we turn to binary-outcome models. We start by stressing the ubiquity and centrality of interdependence in binary outcomes of interest to political and social scientists and note that, again, this interdependence has been ignored in most contexts where it likely arises and that, in the few contexts where it has been acknowledged, the endogeneity of the spatial lag has not be recognized. Next, we explain some of the severe challenges for empirical analysis posed by spatial interdependence in binary-outcome models, and then we follow recent advances in the spatial-econometric literature to suggest Bayesian or recursive-importance-sampling (RIS) approaches for tackling estimation. In brief and in general, the estimation complications arise because among the RHS variables is an endogenous weighted spatial-lag of the unobserved latent outcome, y*, in the other units; Bayesian or RIS techniques facilitate the complicated nested optimization exercise that follows from that fact. We also advance that literature by showing how to calculate estimated spatial effects (as opposed to parameter estimates) in such models, how to construct confidence regions for those (adopting a simulation strategy for the purpose), and how to present such estimates effectively.