bvar_conj_lambda2hyper: Create prior hyperparameters from lambdas
In bdemeshev/bvarr: Package for Bayesian Vector Auto Regressions

Description Usage Arguments Details Value Examples

Create prior hyperparameters from lambdas

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bvar_conj_lambda2hyper(Y_in, Z_in = NULL, constant = TRUE, p = 4,
  lambda = c(0.2, 1, 1, 1, 100, 100), delta = 1, s2_lag = NULL,
  y_bar_type = c("initial", "all"), carriero_hack = FALSE)

`Y_in`	multivariate time series
`Z_in`	exogeneous variables
`constant`	logical, default is TRUE, whether the constant should be included
`p`	number of lags
`lambda`	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 valid for NO sc/io case: 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_bar is multiplied by delta componentwise. By default delta is equal to 1.
`s2_lag`	number of lags in AR() model used to estimate s2 (equal to p by default) Carriero uses 1 in his matlab code http://cremfi.econ.qmul.ac.uk/efp/info.php
`y_bar_type`	(either "all" or "initial"). Determines how y_bar for sc and io dummy is calculated. "all": y_bar is mean of y for all observations, "initial": p initial observations Carriero: all, Sim-Zha: initial
`carriero_hack`	logical, if TRUE sigma^2 will be estimated using biased estimator and supposed error with no square roots in dummy observations will be reproduced FALSE by default

Create prior hyperparameters from lambdas. Lambdas specification is based on Carriero "Bayesian VARs: Specification Choices and Forecast Accuracy" section 3.2.

dummy list containing: Omega, Omega_root S, Phi