Carriero_priors: Set conjugate N-IW priors from lambdas as in Carriero
In bdemeshev/bvarr: Package for Bayesian Vector Auto Regressions

Description Usage Arguments Details Value Examples

Set conjugate N-IW priors from lambdas as in Carriero

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Carriero_priors(Y_in, Z_in = NULL, constant = TRUE, p = 4,
  lambdas = c(0.2, 1, 1, 1, 100, 100), delta = 1, s2_lag = NULL,
  y_0_bar_type = c("initial", "all"))

`Y_in`	[T_in x m] multivariate time series
`Z_in`	[T_in x ...] exogeneous variables
`constant`	logical, default is TRUE, whether the constant should be included
`p`	number of lags
`lambdas`	vector = (l_1, l_lag, l_sc, l_io, l_const, l_exo), the l_kron is set to 1 automatically for conjugate N-IW prior. Short summary: sd(const in eq i) = l_const * sigma_i sd(exo in eq i)= l_exo * sigma_i sd(coef for var j lag l in eq i) = l_1*sigma_i/sigma_j/l^l_lag lambdas may be Inf l_io or l_sc equal to NA means no corresponding dummy observations
`delta`	vector [m x 1] or scalar or 'AR1'. Are used for prior Phi_1 and in sc/io dummy observations Scalar value is replicated m times. If set to 'AR1' then deltas will be estimated as AR(1) coefficients (but not greater than one). Diagonal of Phi_1 is equal to delta. y_0_bar is multiplied by delta componentwise.
`s2_lag`	number of lags in AR() model used to estimate s2 (equal to p by default) Carriero uses 1 in his matlab code
`y_0_bar_type`	(either 'all' or 'initial'). Determines how y_bar0 for sc and io dummy is calculated. 'all': y_bar0 is mean of y for all observations, 'initial': p initial observations Carriero: all, Sim-Zha: initial

Set conjugate N-IW priors from lambdas as in Carriero Based on Carriero "Bayesian VARs: Specification Choices and Forecast Accuracy" section 3.2

priors list containing Phi_prior [k x m], Omega_prior [k x k], S_prior [m x m], v_prior [1x1], where k = mp+d

data(Yraw)
priors <- Carriero_priors(Yraw, p = 4, lambdas = c(0.2, 1, 1, 1, 100, 100))
model <- bvar_conjugate0(priors = priors, keep = 100)
# if something goes wrong then we need info!
model <- bvar_conjugate0(priors = priors, keep = 10, verbose = TRUE)