Bayesian hierarchical vector autoregressive models for patient-level predictive modeling

Bayesian Estimates for Vector - Autoregressive Models

This paper examines frequentist risks of Bayesian estimates of VAR regression coefficient and error covariance matrices under competing loss functions, under a variety of non-informative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix an asymmetric LINEX estimator does better overall than the posterior mean. For the error covari...

Explosive Roots in Level Vector Autoregressive Models ∗

Level vector autoregressive (VAR) models are used extensively in empirical macroeconomic research. However, estimated level VAR models may contain explosive roots, which is at odds with the widespread consensus among macroeconomists that roots are at most unity. This paper investigates the frequency of explosive roots in estimated level VAR models in the presence of stationary and nonstationary...

Bayesian averaging of classical estimates in asymmetric vector autoregressive models

Geo-level Bayesian Hierarchical Media Mix Modeling

Media mix modeling is a statistical analysis on historical data to measure the return on investment (ROI) on advertising and other marketing activities. Current practice usually utilizes data aggregated at a national level, which often suffers from small sample size and insufficient variation in the media spend. When sub-national data is available, we propose a geo-level Bayesian hierarchical m...

Vector Autoregressive Models