Dear all,
I have a quick question about timing of the covariance matrix in dynare
I have the standart definition for SDF
- {{\Lambda}}_{t}={{\beta}}\, \frac{{{U^{C}}}_{t}}{{{U^{C}}}_{t-1}}
and the standard Euler Equation
- 1= \mathbb{E}[\Lambda_{t+1}\, R_{t+1}]
I am confused whether oo_.gamma_y{2,1}(strmatch(‘Lambda’,M_.endo_names,‘exact’),strmatch(‘R’,M_.endo_names,‘exact’); gives me
- =cov[\Lambda_{t+1},R_{t+1}]
or
- cov[\Lambda_{t},R_{t+1}]=cov[\Lambda_{t+1},R_{t}]
if the latter is correct then in order to get cov[\Lambda_{t+1},R_{t+1}]
do i need redefine two auxiliary variables ie
Lambda_tp1=Lambda(+1)
R_tp1=R(+1)
and then look for
oo_.gamma_y{1,1}(Lambda_tp1,R_tp1)
I tried both ways and I got different results so wanted to ask you
Thanks for your help
Regards
You should be getting Cov(\Lambda_t,R_t), which for covariance stationary series is the same as Cov(\Lambda_t,R_t), because we are considering unconditional moments.
Dear Professor Johannes,
thanks for the reply. I attached your dynare code for replicating ‘Benjamin Born and Johannes Pfeifer (2014): “Risk Matters: A comment”, American Economic Review’
I just added two auxiliary variables:
exp(lambda_TP1)=exp(lambda(+1)); line 218
r_tp1=r(+1); line 219
then I look at covariance of
oo_.gamma_y{1,1}(POS.lambda,POS.r)=cov(\Lambda_{t},R^{*}_{t})=0.0019;
oo_.gamma_y{1,1}(POS.lambda_TP1,POS.r_tp1)=cov(\Lambda_{t+1},R^{*}_{t+1})=0.0017
They have different values. Is it because with auxiliary variables in fact I have
cov(\mathbb{E}[\Lambda_{t+1}],\mathbb{E}[R^{*}_{t+1}]) (but covariance of two scalars (Expectations) should be 0)
or is it because it is open economy model with a unit root?
Maybe it is something very trivial that I am missing.
What I am after is the risk adjustment effect after linearizing the model around the risky steady state (not \Lambda_{t} but around \Lambda_{t+1}) that is why I am looking for the cov(\Lambda_{t+1},R^{*}_{t+1})
Using the answer you provided that for cov-stationary series cov(\Lambda_{t},R^{*}_{t})=cov(\Lambda_{t \pm i},R^{*}_{t \pm i}). I just wanted to be sure before I proceed further
Thanks a lot for your help and wish you a wonderful weekend
Regards
Born_Pfeifer_RM_Comment.mod (9.1 KB)
I guess it’s all about the information set. There is a difference between the unconditional covariance between R_t and \Lambda_t, cov(R_t,\Lambda_t) and the unconditional covariance between the conditional expectations of the same variables cov(E_t(R_{t+1}),E_t(\Lambda_{t+1})). The latter is what you get with your auxiliary variables.