Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models

  • Cargnoni C
  • Müller P
  • West M
  • 36


    Mendeley users who have this article in their library.
  • 37


    Citations of this article.


Abstract We consider inference in the class of conditionally Gaussian dynamic models for nonnormal multivariate time series. In such models, data are represented as drawn from nonnormal sampling distributions whose parameters are related both through time and hierarchically across several multivariate series. A key example—the main focus here—is time series of multinomial observations, a common occurrence in sociological and demographic studies involving categorical count data. However, we present this development in a more general setting, as the resulting methods apply beyond the multinomial context. We discuss inference in the proposed model class via a posterior simulation scheme based on appropriate modifications of existing Markov chain Monte Carlo algorithms for normal dynamic linear models and including Metropolis-Hastings components. We develop an analysis of time series of flows of students in the Italian secondary education system as an illustration of the models and methods.

Author-supplied keywords

  • Categorical data
  • Hierarchical dynamic model
  • Markov chain Monte Carlo
  • Posterior simulation
  • State-space model

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text


  • Claudia Cargnoni

  • Peter Müller

  • Mike West

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free