sets up conjugate Normal-Wishart prior
set_prior_cnw(mydata = NULL, factordata = NULL, no_factors = 0, coefprior = NULL, coefpriorvar = NULL, varprior = NULL, varpriordof = NULL, nolags = 1, intercept = TRUE)
| mydata | a TxK xts-object needed for setting up the prior |
|---|---|
| factordata | for factor models additional time series for the factors are needed (not yet implemented) |
| no_factors | number of factors in a factor model (not yet implemented) |
| coefprior | double or () matrix with the prior for the VAR-coefficients. If only a scalar variable is provided the prior will be set to |
| coefpriorvar | double or () matrix with the prior on the variance of the VAR-coefficients. If only a scalar is provided the prior will be set to diag(1,K) |
| varprior | double or () matrix with the prior on Variance-Covariance matrix. |
| varpriordof | integer. The degree of freedom for prior on the Variance-Covariance matrix. |
| nolags | integer Number of lags in the VAR model |
| intercept | logical whether the VAR model has an intercept (TRUE) or not (FALSE) |
returns an S3-object of class "cnw"
The conjugate Normal-Wishart prior
K. Rao Kadiyala and Sune Karlsson, Numerical Methods for Estimation and Inference in Bayesian VAR-Models, Journal of Applied Econometrics 12(2), 99-132
Gary Koop and Dimitris Korobilis (2010), Bayesian Multivariate Time Series Methods for Empirical Macroeconomics, Foundations and Trends in Econometrics 3(4), 267-358