minnesota_prior {bvartools} | R Documentation |
Minnesota Prior
Description
Calculates the Minnesota prior for a VAR model.
Usage
minnesota_prior(
object,
kappa0 = 2,
kappa1 = 0.5,
kappa2 = NULL,
kappa3 = 5,
max_var = NULL,
coint_var = FALSE,
sigma = "AR"
)
Arguments
object |
an object of class |
kappa0 |
a numeric specifying the prior variance of coefficients that correspond to own lags of endogenous variables. |
kappa1 |
a numeric specifying the size of the prior variance of endogenous
variables, which do not correspond to own lags, relative to argument |
kappa2 |
a numeric specifying the size of the prior variance of non-deterministic exogenous
variables relative to argument |
kappa3 |
a numeric specifying the size of the prior variance of deterministic
terms relative to argument |
max_var |
a positive numeric specifying the maximum prior variance that is allowed for
coefficients of non-deterministic variables. If |
coint_var |
a logical specifying whether the model is a cointegrated VAR model, for which the prior means of first own lags should be set to one. |
sigma |
either |
Details
The function calculates the Minnesota prior of a VAR model. For the endogenous variable
the prior variance of the
th lag of regressor
is obtained as
where is the residual standard deviation of variable
of an unrestricted
LS estimate. For exogenous variables
is the sample standard deviation.
For VEC models the function only provides priors for the non-cointegration part of the model. The
residual standard errors are based on an unrestricted LS regression of the
endogenous variables on the error correction term and the non-cointegration regressors.
Value
A list containing a matrix of prior means and the precision matrix of the cofficients and the inverse variance-covariance matrix of the error term, which was obtained by an LS estimation.
References
Chan, J., Koop, G., Poirier, D. J., & Tobias, J. L. (2020). Bayesian Econometric Methods (2nd ed.). Cambridge: University Press.
Lütkepohl, H. (2006). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.
Examples
# Load data
data("e1")
data <- diff(log(e1))
# Generate model input
object <- gen_var(data)
# Obtain Minnesota prior
prior <- minnesota_prior(object)