Description Usage Arguments Value References See Also Examples
Estimate large Bayesian Vector Autorregressive models from Banbura et al. (2010)
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Y |
Time-series matrix or data.frame with the VAR endogenous variables. |
p |
Lag order (default = 1). |
delta |
Numeric vector indicating the prior for the autorregressive coefficients (default = 0 for all variables). If the prior is the same for all variables the user may supply a single number. Otherwise the vector must have one element for each variable. |
lambda |
Constant that regulates the importance given to the priors (default = 0.05). If lambda = 0 the model ignores the data and the posterior equal the prior. For bigger lambda the model converges to the OLS estimates. |
xreg |
Exogenous controls. |
ps |
If TRUE the priors on the sum of the coefficients will be included. |
tau |
Controls the shrinkage in the priors on the sum of the coefficients. |
An object with S3 class "HDeconometricsVAR", "lbvar".
coef.by.equation |
Coefficients listed by each VAR equation. |
coef.by.block |
Coefficients separated by blocks (intercepts, lags, exogenous). |
fitted.values |
In-sample fitted values. |
residuals |
The residuals. |
Y |
Supplied endogenous data. |
p |
VAR lag order chosen by the user. |
N |
Number of endogenous variables. |
covmat |
Residuals covariance matrix. |
xreg |
Exogenous controls supplied by the user. |
Ts |
Number of real observations, number of dummy observations and the sum of both. |
delta |
The delta chosen. |
lambda |
The lambda chosen. |
call |
The matched call. |
Banbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector autoregressions. Journal of Applied Econometrics, 25, 71–92.
Garcia, Medeiros and Vasconcelos (2017).
predict
, HDvar
, irf
, fitLambda
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