bvar: Estimate a bayesian vector autoregressive model.

In joergrieger/bvars: Estimation and forecasting of bayesian VAR models

Description

Estimate a bayesian vector autoregressive model.

Usage

bvar(mydata, priorObj, stabletest = FALSE, nreps = 15000,
  burnin = 5000, nthin = 1)

Arguments

mydata	the time series used for estimating the VAR model
priorObj	a S3-object containing information about the prior
stabletest	logical, flag to test whether a draw is stable or not
nreps	number of draws for the mcmc sampler
burnin	number of burnin-draws
nthin	thinning parameter

Details

This function is the main function to estimate a bayesian VAR model for the TxK series mydata. To estimate a bayesian VAR model to user first has to initialize a prior and select the parmameters for it and submit it to the bvar-function. Internally, bvar first initializes the mcmc-algorithm by calling the function initialize_mcmc then at every subsequent iteration calls the function draw_posterior using the results from the previous posterior draw and the variables Alpha and Sigma return posterior draws of the coefficients and the variance-covariance matrix respectively. All other results are stored in a list named addInfo.

It should be noted that the data submitted to the bvar function and the prior have to be the same. The logical parameter stabletest tells the function whether to check if a draw of coefficients is stable, i.e. if the largest eigenvalue of the companion matrix smaller than 1. Furthermore, the parameters nreps, burnin and nthin determine the number of mcmc-draws and the how many of the draws are retained. The number of retained draws is (nreps - burnin)/nthin.

Value

returns an S3 object of the class "bvar" with the following fields

'general_info' list with general information about the model

'intercept' whether the model has an intercept or not

'nolags' number of lags in the model

'nreps' total number of draws

'burnin' number of burn-in draws

'nthin' the thinning parameter

'data_info' information about the data

'type' type of the data object (can be ts, xts or matrix)

'var_names' variable names

'mydata' the data itself

'mcmc_draws' the draws from the mcmc algortithm

'Alpha' an (K * p + Intercept) x K x (nreps - burnin) / nthin matrix with the draws for the VAR-coefficients. With K being the number of variables, p the number of lags and Intercept is 1 if the model has an intercept and 0 otherwise.

'Sigma' an K x K x (nreps - burnin) / nthin - matrix with the draws of the Variance-Covariance matrix

'additional_info' an array of length (nreps - burnin) / nthin of lists with any additional information returned by the posterior.

See Also

msvar for regime switching models, tvar for threshold models and favar for factoraugmented models.

joergrieger/bvars documentation built on July 6, 2020, 11:51 a.m.