Seminario

Seminario Datalab

Latent Factor Bayesian Multivariate INAR models

Ponente:  Refik Soyer (George Washington University)
Fecha:  jueves 23 de febrero de 2023 - 11:30
Lugar:  Aula Gris 2, ICMAT

Resumen:

We introduce a new class of multivariate integer-valued autoregressive (INAR) models based on the notion of a common random environment. Dependence among the components of the multivariate time series is induced via the common environment that follows a Markovian evolution. The proposed framework provides us with a dynamic multivariate generalization of the univariate INAR processes. We develop a Markov chain Monte Carlo method as well as a particle learning algorithm for Bayesian inference.  We consider an extension of the model to handle zero inflated time-series data and illustrate the proposed class of models using actual multivariate count data and discuss their predictive performance.

(Joint work with Di Zhang, George Washington University and Hedibert Lopes, Arizona State University).