Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999

By Andrew D. Martin, Kevin M. Quinn. Political Analysis, 2002

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The Microfoundations of Mass Polarization (2009, background): “These scholars contend that if there has been any increase in polarization, it has been quite small (see also Wolfe 1999; Baker 2005; McCarty, Poole, and Rosenthal 2006).”
MCMCpack: Markov chain Monte Carlo in R (2011, background): “The interest in Bayesian methods in the social sciences is growing, and a number of researchers are using these approaches in substantive applications dealing with: deliberative bodies (Clinton, Jackman, and Rivers 2004; Martin and Quinn 2002), economic performance (Western 1998), income dynamics (G”
Bayesian analysis of dynamic item response models in educational testing (2013, background): “Martin and Quinn (2002) modeled the transition of a voting preference as a first-order Markov process, where they assumed voting preference changes from the previous time point to a new point by a random shock; this work did not incorporate a time trend.”
The Dynamics of State Policy Liberalism, 1936–2014 (2016, methodology): “…(Bafumi et al. 2005).12 We further constrain the polarity by assigning an informed prior to the policy measure for four states in year t = 0 (Martin and Quinn 2002).13 We estimated the model using the program Stan, as called from R (Stan Development Team 2013; R Core Team 2013).14 Running…”