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.bayesvarmcreates Bayesian VAR models with a Minnesota prior regularization structure. 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.

Objects

`normalbvarm`	Bayesian vector autoregression (VAR) model with normal conjugate prior and fixed covariance for data likelihood
`conjugatebvarm`	Bayesian vector autoregression (VAR) model with conjugate prior for data likelihood
`semiconjugatebvarm`	Bayesian vector autoregression (VAR) model with semiconjugate prior for data likelihood
`diffusebvarm`	Bayesian vector autoregression (VAR) model with diffuse prior for data likelihood
`empiricalbvarm`	Bayesian vector autoregression (VAR) model with samples from prior or posterior distribution

Functions

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Create Model

bayesvarm Create prior Bayesian vector autoregression (VAR) model object

Estimate Posterior

`estimate`	Estimate posterior distribution of Bayesian vector autoregression (VAR) model parameters
`summarize`	Distribution summary statistics of Bayesian vector autoregression (VAR) model

Sample From Posterior

`simsmooth`	Simulation smoother of Bayesian vector autoregression (VAR) model
`simulate`	Simulate coefficients and innovations covariance matrix of Bayesian vector autoregression (VAR) model

Generate Minimum Mean Square Error Forecasts

forecast Forecast responses from Bayesian vector autoregression (VAR) model