Bayesian Vector Autoregression Models

Posterior estimation and simulation using a variety of prior models for VARX model coefficients and innovations covariance matrix

A Bayesian vector autoregression (VAR) model assumes a prior probability distribution on all model coefficients (AR coefficient matrices, model constant vector, linear time trend vector, and exogenous regression coefficient matrix) and the innovations covariance matrix. When combined with data to form a posterior distribution, this framework can lead to a more flexible model and intuitive inferences.

To start a Bayesian VAR analysis, create the prior model object that best describes your prior assumptions on the joint distribution of the coefficients and innovations covariance matrix. bayesvarm creates Bayesian VAR models with a Minnesota prior regularization structure [124]. Then, using the prior model and data, estimate characteristics of the posterior distributions, simulate from the posterior distributions, or forecast responses using the predictive posterior distribution.


normalbvarmBayesian vector autoregression (VAR) model with normal conjugate prior and fixed covariance for data likelihood
conjugatebvarmBayesian vector autoregression (VAR) model with conjugate prior for data likelihood
semiconjugatebvarmBayesian vector autoregression (VAR) model with semiconjugate prior for data likelihood
diffusebvarmBayesian vector autoregression (VAR) model with diffuse prior for data likelihood
empiricalbvarmBayesian vector autoregression (VAR) model with samples from prior or posterior distribution


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bayesvarm Create prior Bayesian vector autoregression (VAR) model object
estimateEstimate posterior distribution of Bayesian vector autoregression (VAR) model parameters
summarizeDistribution summary statistics of Bayesian vector autoregression (VAR) model
simsmoothSimulation smoother of Bayesian vector autoregression (VAR) model
simulateSimulate coefficients and innovations covariance matrix of Bayesian vector autoregression (VAR) model
forecastForecast responses from Bayesian vector autoregression (VAR) model