Dynare forums

Thank you, For the second point I meant If it was possible to have an unanticipated shock in the future ?

Estimation allows unanticpated shocks at any point in time in the future. If you are asking whether you can pre-specify the value of an unanticipated shock happening at a future point in time that is taken as given in estimation, the answer is no.

What about the scope of pre-specified values of unanticipated shocks in the deterministic and stochastic models and just analyzing their transition to a new steady state ?
before any estimation takes place.

Also regarding your paper -
https://sites.google.com/site/pfeiferec ... edirects=1

My model is calibrated with weekly estimates of parameters and the steady states correspond to the weekly real world values of endogenous variables.
However, my data is the monthly unemployment rate.
I was wondering if this would lead to spurious estimation results. But your paper mentions that for a stock variable the length of the model period or frequency does not matter. And since unemployment rate is an average or stock variable this should not affect the estimation. Am i correct ?

Or should I use the kalman filter with missing data for some weeks as mentioned in your paper ?
My observations are monthly unemployment rates.

1. What do you mean with scope? I don't understand your question. Maybe it would help if you could tell me what you are actually trying to achieve.

2. I have to clarify this in my guide. You have to get the timing correct. The monthly unemployment rate and the weekly variables need to be consistent. The answer to your question is in the definition of the unemployment rate. In the US, it comes from the CPS survey. From http://www.bls.gov/cps/cps_htgm.htm

Each month, highly trained and experienced Census Bureau employees contact the 60,000 eligible sample households and ask about the labor force activities (jobholding and job seeking) or non-labor force status of the members of these households during the survey reference week (usually the week that includes the 12th of the month).

Thus, the unemployment rate observed is the one in one particular week. The values for all other weeks are missing. For other countries, the definition might be different.

I am trying to model a DMP model with endogenous search effort and unemployment benefits. I want the job destruction rate to increase from .0081 to .02 in the 12th period. Where the job destruction rate in an exogenous shock. Since it is a perfect foresight model in the deterministic setup the agents know about all future shocks and might adjust their behaviour in the first 11 periods as they know that the shock will arrive in 12th period.

Is there any way such that this anticipatory behaviour in the first 11 periods be avoided ?

In this case, you need to set up the model as a stochastic one. Why in period 11? Is there meaningful transition going on before that requires you to study the transition behavior after an increase in the job destruction rate only after an initial period?
If so, you could use a perturbation-solved stochastic model together with your own simulation using the simult_-command.

Or are you trying to impose an unanticipated parameter switch during estimation?

I want to have no anticipatory behaviour of the agents before the 12th period deterministic shock.

Thanks a lot.