IRIS Toolbox Reference Manual

## litterman

Litterman's prior dummy observations for BVARs

#### Syntax

    O = BVAR.litterman(Rho,Mu,Lmb)

#### Input arguments

- `Rho` [ numeric ] - White-noise priors (`Rho = 0`) or random-walk priors (`Rho = 1`).

- `Mu` [ numeric ] - Weight on the dummy observations.

- `Lmb` [ numeric ] - Exponential increase in weight depending on the lag; `Lmb = 0` means all lags are weighted equally.

#### Output arguments

- `O` [ bvarobj ] - BVAR object that can be passed into the `VAR/estimate` function.

#### Description

See the section explaining the weights on prior dummies, i.e. the input argument `Mu`.

#### Example

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