question about welfare calculation in a JME paper
Posted: Mon Jan 16, 2017 3:26 pm
Dear all,
I am confused on the calculation method in the paper of “Capital controls and optimal Chinese monetary policy“, which is published on JME 2015.
Here I attach the DYNARE CODE and the paper downloaded from the author's website.
And the key part of the code is below:
planner_objective(C + log(C_ss) - (Phi_l*L_ss^(1+eta)/(1+eta))*exp((1+eta)*L));
ramsey_policy(planner_discount=1, nograph, noprint, irf=20, periods=1000, instruments=(R));
welf = -(1/(1-beta))*C_ss*Phi_l*(eta/2)*L_ss^(eta-1)*oo_.var(2,2);
Here comes by question:
Since in they write their model in log_liearizing way, why they define welfare in a recursive way which Prof. Pleifer said should only be used in no-linear model? And what is the relationship between a recursively defined welfare function in the paper and the welfare calculation equation in the MOD file, which is "welf = -(1/(1-beta))*C_ss*Phi_l*(eta/2)*L_ss^(eta-1)*oo_.var(2,2);". And the latter seemingly only cares about the variance of labor.
Thanks a lot.
I am confused on the calculation method in the paper of “Capital controls and optimal Chinese monetary policy“, which is published on JME 2015.
Here I attach the DYNARE CODE and the paper downloaded from the author's website.
And the key part of the code is below:
planner_objective(C + log(C_ss) - (Phi_l*L_ss^(1+eta)/(1+eta))*exp((1+eta)*L));
ramsey_policy(planner_discount=1, nograph, noprint, irf=20, periods=1000, instruments=(R));
welf = -(1/(1-beta))*C_ss*Phi_l*(eta/2)*L_ss^(eta-1)*oo_.var(2,2);
Here comes by question:
Since in they write their model in log_liearizing way, why they define welfare in a recursive way which Prof. Pleifer said should only be used in no-linear model? And what is the relationship between a recursively defined welfare function in the paper and the welfare calculation equation in the MOD file, which is "welf = -(1/(1-beta))*C_ss*Phi_l*(eta/2)*L_ss^(eta-1)*oo_.var(2,2);". And the latter seemingly only cares about the variance of labor.
Thanks a lot.