Introducing Covariances in the Objective Function of OSR
Posted: Sat Oct 25, 2014 7:22 pm
Dear Dynare Community,
I am working with optimal simple monetary and fiscal rules using the osr command. My objective function is a loss function and usually after running osr I compute the asymptotic loss and compare it with the osr objective function value. Although it is possible to introduce covariances in the osr objective function, I notice that when do it the asymptotic loss does not match the osr objective function value.
Example:
Suppose my objective function is: alpha*(y^2) + omega*(pi^2)
osr code:
optim_weights;
y alpha;
pi omega;
end;
In this case, the osr objective function value is the same as the asymptotic loss: alpha*variance(y) + omega*variance(pi)
However, if my objective function is: alpha*(y^2) + omega*(pi^2) + 2*chi*y*pi
osr code:
optim_weights;
y alpha;
pi omega;
y,pi chi;
end;
In this case, the osr objective function value is not the same as the asymptotic loss: alpha*variance(y) + omega*variance(pi) + 2*chi*covariance(y,pi)
Any help?
I thank in advance your reply.
Best,
Paulo
I am working with optimal simple monetary and fiscal rules using the osr command. My objective function is a loss function and usually after running osr I compute the asymptotic loss and compare it with the osr objective function value. Although it is possible to introduce covariances in the osr objective function, I notice that when do it the asymptotic loss does not match the osr objective function value.
Example:
Suppose my objective function is: alpha*(y^2) + omega*(pi^2)
osr code:
optim_weights;
y alpha;
pi omega;
end;
In this case, the osr objective function value is the same as the asymptotic loss: alpha*variance(y) + omega*variance(pi)
However, if my objective function is: alpha*(y^2) + omega*(pi^2) + 2*chi*y*pi
osr code:
optim_weights;
y alpha;
pi omega;
y,pi chi;
end;
In this case, the osr objective function value is not the same as the asymptotic loss: alpha*variance(y) + omega*variance(pi) + 2*chi*covariance(y,pi)
Any help?
I thank in advance your reply.
Best,
Paulo