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Multilevel multivariate modelling of legislative count data, with a hidden Markov chain

Author

Listed:
  • Francesco Lagona

    (ROMA TRE - Università degli Studi Roma Tre = Roma Tre University)

  • Antonello Maruotti

    (University of Southampton)

  • Fabio Padovano

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, ROMA TRE - Università degli Studi Roma Tre = Roma Tre University)

Abstract

type="main" xml:id="rssa12089-abs-0001"> 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.
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Suggested Citation

  • Francesco Lagona & Antonello Maruotti & Fabio Padovano, 2015. "Multilevel multivariate modelling of legislative count data, with a hidden Markov chain," Post-Print halshs-01246575, HAL.
    • Handle: RePEc:hal:journl:halshs-01246575
    DOI: 10.1111/rssa.12089
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01246575
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    References listed on IDEAS

    as
    1. Francesco Lagona & Fabio Padovano, 2008. "The political legislation cycle," Public Choice, Springer, vol. 134(3), pages 201-229, March.
    2. Josef Brechler & Adam Geršl, 2014. "Political legislation cycle in the Czech Republic," Constitutional Political Economy, Springer, vol. 25(2), pages 137-153, June.
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    Cited by:

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    2. Timo Adam & Roland Langrock & Christian H. Weiß, 2019. "Penalized estimation of flexible hidden Markov models for time series of counts," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 87-104, August.
    3. Lagona, Francesco & Padovano, Fabio, 2021. "How does legislative behavior change when the country becomes democratic? The case of South Korea," European Journal of Political Economy, Elsevier, vol. 69(C).
    4. François Facchini & Elena Seghezza, 2021. "Legislative production and public spending in France," Public Choice, Springer, vol. 189(1), pages 71-91, October.
    5. Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    6. W. H. Bonat & J. Olivero & M. Grande-Vega & M. A. Farfán & J. E. Fa, 2017. "Modelling the Covariance Structure in Marginal Multivariate Count Models: Hunting in Bioko Island," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 446-464, December.
    7. Fabio Padovano & Youssoufa Sy, 2023. "Conditional Political legislation cycles," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2023-02-ccr, Condorcet Center for political Economy.
    8. Maruotti, Antonello & Punzo, Antonio, 2017. "Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 475-496.
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    10. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.

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