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MSBVAR (version 0.3.1)

Markov-Switching Bayesian Vector Autoregression Models

Description

Provides methods for estimating frequentist and Bayesian Vector Autoregression (VAR) models. Functions for reduced form and structural VAR models are also available. Includes methods for the generating posterior inferences for VAR forecasts, impulse responses (using likelihood-based error bands), and forecast error decompositions. Also includes utility functions for plotting forecasts and impulse responses, and generating draws from Wishart and singular multivariate normal densities. Future versions will include some models with Markov switching.

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Version

Install

install.packages('MSBVAR')

Monthly Downloads

19

Version

0.3.1

License

GPL version 2 or newer

Maintainer

Patrick Brandt

Last Published

February 15th, 2017

Functions in MSBVAR (0.3.1)

summary

Summary functions for VAR / BVAR / B-SVAR model objects
plot.forc.ecdf

Plots VAR forecasts and their empirical error bands
dfev

Decompositions of Forecast Error Variance (DFEV) for VAR/BVAR/BSVAR models
BCFdata

Subset of Data from Brandt, Colaresi, and Freeman (2007)
null.space

Find the null space of a matrix
forc.ecdf

Empirical CDF computations for posterior forecast samples
var.lag.specification

Automated VAR lag specification testing
forecast

Generate forecasts for fitted VAR objects
IsraelPalestineConflict

Weekly Goldstein Scaled Israeli-Palestinian Conflict Data, 1979-2003
rwishart

Random deviates from a Wishart distribution
A02mcmc

Converts A0 objects to coda MCMC objects
reduced.form.var

Estimation of a reduced form VAR model
rmultnorm

Multivariate Normal Random Number Generator
print.dfev

Printing DFEV tables
plot.gibbs.A0

Plot a parameter density summary for B-SVAR A(0) objects
print.posterior.fit

Print method for posterior fit measures
mae

Mean absolute error of VAR forecasts
plot.mc.irf

Plotting posteriors of Monte Carlo simulated impulse responses
plot.forecast

Plots competing sets of VAR forecasts or a single set of VAR forecasts
restmtx

Utility function for generating the restriction matrix for hard condition forecasting
rmse

Root mean squared error of a Monte Carlo / MCMC sample of forecasts
hc.forecast

Forecast density estimation of hard condition forecasts for VAR models via MCMC
cf.forecasts

Compare VAR forecasts to each other or real data
SZ.prior.evaluation

Sims-Zha Bayesian VAR Prior Specification Search
mountains

Mountain plots for summarizing forecast densities
irf

Impulse Response Function (IRF) Computation for a VAR
mcmc.szbsvar

Gibbs sampler for coefficients of a B-SVAR model
granger.test

Bivariate Granger causality testing
plot.irf

Plots impulse responses
szbvar

Reduced form Sims-Zha Bayesian VAR model estimation
normalize.svar

Likelihood normalization of SVAR models
gibbs.A0

Gibbs sampler for posterior of Bayesian structural vector autoregression models
mc.irf

Monte Carlo Integration / Simulation of Impulse Response Functions
szbsvar

Structural Sims-Zha Bayesian VAR model estimation
decay.spec

Lag decay specification check
posterior.fit

Estimates the marginal likelihood and posterior probability for VAR, BVAR, and BSVAR models
uc.forecast

Forecast density estimation unconditional forecasts for VAR/BVAR/BSVAR models via MCMC