Replication data for: Endogenous Jurisprudential Regimes

In this paper we offer a multivariate multiple change-point probit model that can be used to endogenously test for the existence of jurisprudential regimes. Unlike the previously employed methods, our model does so by estimating the locations of many possible change-points along with structural parameters. We estimate the model using Markov chain Monte Carlo methods, and use Bayesian model comparison to determine the number of change-points. Our findings are consistent with jurisprudential regimes in the Establishment Clause and administrative law contexts. We find little support for hypothesized regimes in the areas of free speech and search and seizure. The Bayesian multivariate change-point model we propose has broad potential applications to studying structural breaks in either regular or irregular time series data about political institutions or processes. The replication materials include four datasets and R code for estimating the multivariate profit change-point model and calculating Bayes factor.

本文提出一种可用于内生检验法理范式存在性的多元多重变点概率单位（probit）模型。与此前采用的方法不同，本模型可通过同时估计诸多潜在变点的位置与结构参数来完成该检验。本文采用马尔可夫链蒙特卡洛（Markov chain Monte Carlo）方法对模型进行估计，并借助贝叶斯模型比较来确定变点的数量。研究结果契合确立条款（Establishment Clause）与行政法语境下的法理范式，而在言论自由及搜查扣押领域，并未为相关假设的范式提供多少支撑。本文所提出的贝叶斯多元变点模型，可广泛应用于研究政治制度或政治进程相关的规则或不规则时序数据中的结构突变。本研究的复现材料包含四个数据集，以及用于估计多元变点概率单位模型并计算贝叶斯因子（Bayes factor）的R代码。