  This algorithm extends Ljungqvist and Sargent (2012) algorithm of Stackelberg
dynamic game to the case of dynamic stochastic general equilibrium models
including exogenous forcing variables. It is based Anderson, Hansen, McGrattan,
Sargent (1996) discounted augmented linear quadratic regulator. It adds an
intermediate step in solving a Sylvester equation. Forward-looking variables
are also optimally anchored on forcing variables. This simple algorithm calls
for already programmed routines for Ricatti, Sylvester and Inverse matrix in
Matlab and Scilab. A final step using a change of basis vector computes a
vector auto regressive representation including Ramsey optimal policy rule
function of lagged observable variables, when the exogenous forcing variables
are not observable.
