My model uses operators like max, min, abs, sign, <, >, <=, >=, ==, != which is discouraged in stoch_simul. I want to generate the IRFs of the model. In the manual, it is said that extended_path is not affected by this problem. But I could not find an example to see how IRF is generated using extended_path.
Is there a sample code to replace IRF generation via stoch_simul with IRF generation via extended_path? Note that I find the steady state of my model using a matlab file.
@stepan-a Is there a working example somewhere?
Any answer to this?
I tried replacing stoch_simul with extended_path in my model, but the steady state values returned by the matlab file are not reflected in the oo_.steady_state object (all values are 0) and then there is no time series generated in oo_.endo_simul probably because the steady state values are all 0.
The model runs fine with stoch_simul. What might be going wrong?
Secondly, to generate IRF, the values in oo_.exo_simul should be (stdev,0,0,…). But extended_path does not generate that by default. What should be done to change it so that it generates the IRF?
Examples are at https://git.dynare.org/Dynare/dynare/tree/master/tests/ep
An IRF in this context would be to define a given shock at time 0 and then compute the corresponding simulation.
Thank you Professor!
I am now able to generate a time series using extended_path just like the one generated using stoch_simul. Now, I want to use this time series to generate the IRF plots as stoch_simul does. Extended_path does not generate such IRF plots by default.
What should I do to generate these IRF plots? Is there a quick way to do that? I see that the values in the time series are not the same as the values in the IRF plots of stoch_simul.
You need to set up the requisite simulation exercise. Start at the steady state, end at the steady state and add a shock in the first period. Then you should be able to use
rplot to graph the result, which is the perfect foresight version of an IRF, i.e. a one-time shock that was not anticipated.
Thank you! Please confirm that I have understood the steps correctly:
I set DynareResults.steady_state equal to the steady state (which I have to add separately to extended_path as extended_path does not read steady state values from a matlab file by itself)
Fix a T (=300) number of periods for which to run the simulation.
Set DynareResults.exo_simul to the vector (sd, 0, 0, …, 0) of size T where sd is the standard deviation of the shock in the model.
Run the simulation which is in extended_path.m and plot the simulated time series with rplot.
Secondly, I use hp_filter in stoch_simul to smooth the IRF. Is there a way to apply this filter on the time series generated by extended_path?
Also, when running extended_path with model(use_dll); instead of model; , all the values in the time series become NaNs, whereas model; generates the correct values. What might be going wrong?
Sorry for the confusion, I add hp_filter option in stoch_simul, I dont filter IRFs. So that comment of mine can be ignored.
I am using 4.5.7.
When I do stoch_simul with the use_dll option too, the steady state values are not picked up from the matlab file in to the oo_ object, the exo_simul vector is all 0’s and endo_simul is empty; even though stoch_simul runs fine without the use_dll option. To change to the use_dll option, I do two changes: “model(use_dll)” and “dynare mingw fast”. Am I missing something?
does not useoo_.exo_simul`.
Sorry, the model(use_dll) option with stoch_simul does pick up the steady state values in to oo_.dr.ys, but oo_.endo_simul is empty and oo_.irfs is not created. Is something wrong here?
I would need to see the files