17. Bayesian Copula Factor Models for Mixed-Type Time Series Data

We present a Bayesian Copula Factor Autoregressive (BCFAR) model adapted to analyze multivariate time series data with mixed variable types. This approach captures dynamic relationships among macroeconomic indicators and stock market indices by modeling both main effects and interactions through latent factors. The BCFAR framework integrates copula functions with quadratic autoregression to flexibly accommodate conditional dependence structures across continuous and discrete covariates. To improve computational scalability, we adopt a semiparametric extended rank likelihood and develop an efficient MCMC algorithm combining Metropolis-Hastings and Forward Filtering Backward Sampling within a Gibbs sampler. Simulation studies and real-world macroeconomic data analysis demonstrate the accuracy and efficiency of our methods.

BCFAR 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025