Code for accurately solving various kinds of Diamond-Mortenson-Pissarides models for labor-market search.

Our code does not require many libraries beyond numpy, scipy, and statsmodels. The full list of required libraries and their versions is found in requirements.txt. To create a python environment with these libraries, execute the following after cloning the repo:

conda create -n dmp_projection python=3.6
source activate dmp_projection
pip install -r requirements.txt

On OSX, sometimes matplotlib has problems in virtual environments. If this problem arises remove matplotlib from the requirements.txt and replace the first line of the previous snippet with this:

conda create -n dmp_projection python=3.6 matplotlib

If you want to execute the code in an interactive setting, you'll also want to pip install jupyter which will give you ipython, jupyter notebooks and everything else you could want to run the code interactively.

The files for building/solving a standard DMP model are located in the dmp-vanilla folder.

• dmp_model.py : contains model parameters and the most fundmental model equations
• dmp_solver.py: contains the models residual function and a routine for solving it
• dmp_simulator.py: contains routines for simulating the model, computing moments if desired
• dmp_irf.py: contains functions for simulating/plotting impulse response functions
• utils.py: contains generic functions not related to the model

Files for building/solving a DMP model with credit-market frictions are located in the dmp-credit folder. This follow a similar naming schema as above. The idea was to write one dmp_solver.py file for any appropriately organized dmp_model.py file, but I missed the mark a bit so here I am with different versions of the code for different variants of the DMP search model. Hopefully we can fix that in future iterations of this code.