The distributed-lag expectations in dynare /please help me!/

hi everyone;

i’m trying to estimate a SOE model of inflation targeting based on a IMF paper (ref :WP/08/48), and i’m having the following trouble:

there’s an UIP (uncovered interest parity) condition in the model where the expected exchange rate at t+1 is expressed as follows:

z_exp = delta*z(+1) + (1-delta)*z(-1)

z_exp : the expected exchange rate at t+1
z : the real exchange rate

the problem is that, when i use this expression, matlab gives this warning message during estimation : " Matrix is singular to working precision ", and estimation fails by the end (even if i use mode_compute=6).

i use this command: model_diagnostics(M_,options_,oo_), and it gives me:

Warning: Matrix is close to singular or badly scaled. Results may be
inaccurate. RCOND = 1.347220e-018.

In model_diagnostics at 115
model_diagnostic: the Jacobian of the static model is singular
there is 1 colinear relationships between the variables and the equations
Colinear variables:
s // s is the nominal exchange rate
Colinear equations
Columns 1 through 12

 1     2     3     5     6     7    10    11    12    14    17    20

Columns 13 through 16

21    22    26    27

and i don’t know how to deal with this colinearity problem :frowning:

What is a little bit strange is that when use the following trick, estimation works perfectly !:

i’ve tried to declare two parameters (delta_lead & delta_lag) intsead of delta, and i have written z_exp as follows:

z_exp = delta_leadz(+1) + delta_lagz(-1)

however, this is not theoretically correct because delta_lead and delta_lag should sum up to 1. :mrgreen:

So please help me, i’m really stuck and i have to deliver my project after tomorrow !

i’m using dynare 4.2.5, and i’ve tried the 4.2.4 and didn’t work also (i thaught that may be it’s a bug issue)

still have to mention that i’m an undergraduate student and i’m new to dynare :smiley:



Heeeeeeeeelp :cry:

I am sorry, but this is most probably an issue with your model. This type of collinearity problem typically occurs when you have on redundant equation in your model and you are missing another equation. It might occur in your case as the UIP is derived by combining 2 FOCs without eliminating one variable completely.