Multilevel multivariate modelling of legislative count data, with a hidden Markov chain
Multilevel multivariate modelling of legislative count data, with a hidden Markov chain
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具有隐马尔可夫链的立法计数数据的多级多元建模
DOI：
10.1111/rssa.12089
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
F. Padovano
中科院分区：
文献类型：
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作者：
F. Lagona;A. Maruotti;F. Padovano
立法行为的产生受到潜在异质性的多个来源的影响，这是因为多层次和多变量的未观察因素与数据层次所有级别上的观察协变量一起作用。我们通过估计一个多水平泊松回归模型来解释这些因素，该模型用于重复测量行政和普通立法行为的双变量计数，这些行为是在多个意大利政府颁布的、嵌套在立法机构内的。该模型综合了立法机构层面的离散双变量随机效应和政府层面的离散双变量随机效应的马尔可夫序列。它可以通过一种计算上可行的期望最大化算法来估计。它自然地扩展了传统的泊松回归模型，以考虑多结果、纵向相关性和多级数据层次结构。在一个意大利立法生产的案例研究中，该模型被用来检测在多个时间尺度上出现的多个立法供给周期。
The production of legislative acts is affected by multiple sources of latent heterogeneity, due to multilevel and multivariate unobserved factors that operate in conjunction with observed covariates at all the levels of the data hierarchy. We account for these factors by estimating a multilevel Poisson regression model for repeated measurements of bivariate counts of executive and ordinary legislative acts, enacted under multiple Italian governments, nested within legislatures. The model integrates discrete bivariate random effects at the legislature level and Markovian sequences of discrete bivariate random effects at the government level. It can be estimated by a computationally feasible expectation–maximization algorithm. It naturally extends a traditional Poisson regression model to allow for multiple outcomes, longitudinal dependence and multilevel data hierarchy. The model is exploited to detect multiple cycles of legislative supply that arise at multiple timescales in a case‐study of Italian legislative production.