Bayesian Estimation of Structural Vector Autoregressive Models
Verifies identification through heteroskedasticity or non-normality of...
Verifies identification through heteroskedasticity or non-normality of...
Verifies identification through heteroskedasticity or non-normality of...
Bayesian Estimation of Structural Vector Autoregressive Models
Bayesian estimation of a homoskedastic Structural Vector Autoregressio...
Computes posterior draws of structural shock conditional standard devi...
Computes posterior draws of structural shock conditional standard devi...
Computes posterior draws of structural shock conditional standard devi...
Computes posterior draws of structural shock conditional standard devi...
Verifies identification through heteroskedasticity or non-normality of...
Computes posterior draws of structural shock conditional standard devi...
Computes posterior draws of structural shock conditional standard devi...
Computes posterior draws from data predictive density
Computes posterior draws from data predictive density
Bayesian estimation of a Structural Vector Autoregression with shocks ...
Computes posterior draws from data predictive density
Computes posterior draws from data predictive density
Computes posterior draws from data predictive density
Computes posterior draws from data predictive density
Computes posterior draws of historical decompositions
Computes posterior draws of historical decompositions
Computes posterior draws of historical decompositions
Computes posterior draws of historical decompositions
Computes posterior draws of historical decompositions
Bayesian estimation of a Structural Vector Autoregression with Markov-...
Computes posterior draws of historical decompositions
Computes posterior draws of impulse responses
Computes posterior draws of impulse responses
Computes posterior draws of impulse responses
Computes posterior draws of impulse responses
Computes posterior draws of impulse responses
Computes posterior draws of impulse responses
Computes posterior draws of regime probabilities
Computes posterior draws of regime probabilities
Bayesian estimation of a Structural Vector Autoregression with Stochas...
Computes posterior draws of regime probabilities
Computes posterior draws of structural shocks
Computes posterior draws of structural shocks
Computes posterior draws of structural shocks
Bayesian estimation of a homoskedastic Structural Vector Autoregressio...
Computes posterior draws of structural shocks
Computes posterior draws of structural shocks
Computes posterior draws of structural shocks
Computes posterior draws of the forecast error variance decomposition
Bayesian estimation of a homoskedastic Structural Vector Autoregressio...
Computes posterior draws of the forecast error variance decomposition
Computes posterior draws of the forecast error variance decomposition
Computes posterior draws of the forecast error variance decomposition
Computes posterior draws of the forecast error variance decomposition
Computes posterior draws of the forecast error variance decomposition
Bayesian estimation of a Structural Vector Autoregression with shocks ...
Bayesian estimation of a Structural Vector Autoregression with Markov-...
Bayesian estimation of a Structural Vector Autoregression with Stochas...
Bayesian estimation of a homoskedastic Structural Vector Autoregressio...
Bayesian estimation of Structural Vector Autoregressions via Gibbs sam...
Forecasting using Structural Vector Autoregression
Forecasting using Structural Vector Autoregression
Forecasting using Structural Vector Autoregression
Forecasting using Structural Vector Autoregression
Forecasting using Structural Vector Autoregression
Forecasting using Structural Vector Autoregression
Waggoner & Zha (2003) row signs normalisation of the posterior draws f...
Plots the median and an interval between two specified percentiles for...
Plots fitted values of dependent variables
Plots forecast error variance decompositions
Plots fitted values of dependent variables
Plots historical decompositions
Plots impulse responses
Plots estimated regime probabilities
Plots structural shocks
Plots structural shocks' conditional standard deviations
R6 Class representing the specification of the BSVAR model with a zero...
R6 Class representing the specification of the BSVAR model with Markov...
R6 Class representing the specification of the BSVAR model with Stocha...
R6 Class Representing PriorBSVARMIX
R6 Class representing the specification of the BSVAR model with t-dist...
R6 Class representing the specification of the homoskedastic BSVAR mod...
R6 Class Representing DataMatricesBSVAR
R6 Class Representing IdentificationBSVARs
R6 Class Representing PosteriorBSVARMIX
R6 Class Representing PosteriorBSVARMSH
R6 Class Representing PosteriorBSVARSV
R6 Class Representing PosteriorBSVART
R6 Class Representing PosteriorBSVAR
R6 Class Representing PriorBSVARMSH
R6 Class Representing PriorBSVARSV
R6 Class Representing PriorBSVART
R6 Class Representing PriorBSVAR
R6 Class Representing StartingValuesBSVARMIX
R6 Class Representing StartingValuesBSVARMSH
R6 Class Representing StartingValuesBSVARSV
R6 Class Representing StartingValuesBSVART
R6 Class Representing StartingValuesBSVAR
Verifies hypotheses involving autoregressive parameters
Provides posterior summary of Forecasts
Provides posterior summary of homoskedastic Structural VAR estimation
Provides posterior summary of non-normal Structural VAR estimation
Provides posterior summary of heteroskedastic Structural VAR estimatio...
Verifies hypotheses involving autoregressive parameters
Provides posterior summary of heteroskedastic Structural VAR estimatio...
Provides posterior summary of Structural VAR with t-distributed shocks...
Provides posterior summary of forecast error variance decompositions
Provides posterior summary of variables' fitted values
Provides posterior summary of historical decompositions
Provides posterior summary of impulse responses
Provides posterior summary of regime probabilities
Provides posterior summary of structural shocks
Verifies identification through heteroskedasticity or non-normality of...
Provides posterior summary of structural shocks' conditional standard ...
Provides summary of verifying hypotheses about autoregressive paramete...
Provides summary of verifying shocks' normality
Provides summary of verifying homoskedasticity
Verifies identification through heteroskedasticity or non-normality of...
Provides summary of verifying homoskedasticity
Provides summary of verifying shocks' normality
Provides summary of verifying homoskedasticity
Verifies hypotheses involving autoregressive parameters
Verifies hypotheses involving autoregressive parameters
Verifies hypotheses involving autoregressive parameters
Verifies hypotheses involving autoregressive parameters
Verifies heteroskedasticity of structural shocks equation by equation
Verifies heteroskedasticity of structural shocks equation by equation
Verifies heteroskedasticity of structural shocks equation by equation
Verifies heteroskedasticity of structural shocks equation by equation
Verifies heteroskedasticity of structural shocks equation by equation
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>.