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Generalized Dynamic Factor Models for Mixed-Measurement Time Series.

Publication ,  Journal Article
Cui, K; Dunson, DB
Published in: Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
February 2014

In this article, we propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series. The framework allows mixed-scale measurements associated with each time series, with different measurements having different distributions in the exponential family conditionally on time-varying latent factor(s). Efficient Bayesian computational algorithms are developed for posterior inference on both the latent factors and model parameters, based on a Metropolis Hastings algorithm with adaptive proposals. The algorithm relies on a Greedy Density Kernel Approximation (GDKA) and parameter expansion with latent factor normalization. We tested the framework and algorithms in simulated studies and applied them to the analysis of intertwined credit and recovery risk for Moody's rated firms from 1982-2008, illustrating the importance of jointly modeling mixed-measurement time series. The article has supplemental materials available online.

Duke Scholars

Published In

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

DOI

EISSN

1537-2715

ISSN

1061-8600

Publication Date

February 2014

Volume

23

Issue

1

Start / End Page

169 / 191

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Cui, K., & Dunson, D. B. (2014). Generalized Dynamic Factor Models for Mixed-Measurement Time Series. Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 23(1), 169–191. https://doi.org/10.1080/10618600.2012.729986
Cui, Kai, and David B. Dunson. “Generalized Dynamic Factor Models for Mixed-Measurement Time Series.Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 23, no. 1 (February 2014): 169–91. https://doi.org/10.1080/10618600.2012.729986.
Cui K, Dunson DB. Generalized Dynamic Factor Models for Mixed-Measurement Time Series. Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America. 2014 Feb;23(1):169–91.
Cui, Kai, and David B. Dunson. “Generalized Dynamic Factor Models for Mixed-Measurement Time Series.Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 23, no. 1, Feb. 2014, pp. 169–91. Epmc, doi:10.1080/10618600.2012.729986.
Cui K, Dunson DB. Generalized Dynamic Factor Models for Mixed-Measurement Time Series. Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America. 2014 Feb;23(1):169–191.

Published In

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

DOI

EISSN

1537-2715

ISSN

1061-8600

Publication Date

February 2014

Volume

23

Issue

1

Start / End Page

169 / 191

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 1403 Econometrics
  • 0104 Statistics