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Multilevel multivariate modelling of legislative count data, with a hidden Markov chain
Author(s) -
Lagona Francesco,
Maruotti Antonello,
Padovano Fabio
Publication year - 2015
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12089
Subject(s) - bivariate analysis , econometrics , multilevel model , hierarchy , legislature , random effects model , multivariate statistics , poisson regression , markov chain , covariate , count data , statistics , univariate , mathematics , poisson distribution , computer science , economics , geography , population , medicine , meta analysis , demography , archaeology , sociology , market economy
Summary 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.