O = BVAR.litterman(Rho,Mu,Lmb)
Rho
[ numeric ] - White-noise priors (Rho = 0
) or random-walk priors (Rho = 1
), or something in between.
Mu
[ numeric ] - Weight on dummy observations.
Lmb
[ numeric ] - Exponential increase in weight depending on the lag; Lmb = 0
means all lags are weighted equally.
O
[ bvarobj ] - BVAR object that can be passed into the VAR/estimate
function.See the section explaining the weights on prior dummies, i.e. the input argument Mu
.