Skip to main content

Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models

Publication ,  Journal Article
Cargnoni, C; Müller, P; West, M
Published in: Journal of the American Statistical Association
June 1, 1997

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. © 1997 Taylor & Francis Group, LLC.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

June 1, 1997

Volume

92

Issue

438

Start / End Page

640 / 647

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Cargnoni, C., Müller, P., & West, M. (1997). Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models. Journal of the American Statistical Association, 92(438), 640–647. https://doi.org/10.1080/01621459.1997.10474015
Cargnoni, C., P. Müller, and M. West. “Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models.” Journal of the American Statistical Association 92, no. 438 (June 1, 1997): 640–47. https://doi.org/10.1080/01621459.1997.10474015.
Cargnoni C, Müller P, West M. Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models. Journal of the American Statistical Association. 1997 Jun 1;92(438):640–7.
Cargnoni, C., et al. “Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models.” Journal of the American Statistical Association, vol. 92, no. 438, June 1997, pp. 640–47. Scopus, doi:10.1080/01621459.1997.10474015.
Cargnoni C, Müller P, West M. Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models. Journal of the American Statistical Association. 1997 Jun 1;92(438):640–647.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

June 1, 1997

Volume

92

Issue

438

Start / End Page

640 / 647

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics