Code for Galichon-Salanie's "Estimating Separable Matching Models"
Usage
Create a virtual environment, e.g. with python3 -m venv env. Activate it with source env/bin/activate.
Install the requirements with pip install -r requirements.txt. Among the packages it downloads are two created by Bernard Salanié: bs_python_utils and cupid_matching. The former is just a set of utility programs. The latter contains code to solve for the stable matching and estimate the parameters of separable matching models with MDE and Poisson-GLM. The code in this folder relies heavily on these two packages, which are documented on Salanie's website: bs_python_utils and cupid_matching.
Choose the parameters in config.py and run the code with python main.py. The program will create a folder Results and save a plot and a pickled file with the estimates for the sample sample_size defined in config.py.
Each simulation sample (that is, n_sim=1) takes a few seconds (4 seconds on a Mac M2 Max 2023) to estimate the Choo and Siow model by the two methods in the paper --- minimum distance and Poisson GLM. The code is parallelized over samples, unless you choose use_multiprocessing=False. By default, it uses all except 2 of your CPUs.
Structure of the code
The master program main.py reads the parameters in config.py.
- If
do_create_samples is True it uses create_samples.py to read the Choo and Siow datasets in the data_dir directory and to create two samples in samples_dir (both directories are specified in config,.py). The two samples correspond to the small and large samples described in Section 6 of the paper. The files created have the marriage patterns by age (*muxy.txt), the margins (*nx.txt and *my.txt), and the variance-covariance matrix of these estimates (*varmus.pkl).
- It calls
read_data.py, which reads the sample defined by sample_size in config.py and prepares it for the simulation. read_data.py also has code to add a small positive number (see zero_guard in config.py) for zero cells; this is used in the MDE simulation.
specification.py creates the basis functions according to the specification given by degrees in config.py.- Then
main.py runs the simulation via simulate.py as defined by config.py.
Configuration
All parameters of the simulation are in config.py:
do_create_samples, do_simuls, plot_simuls, do_simuls_mde, do_simuls_poisson define what the program does;n_sim is the number of simulated samples; use_multiprocessing and nb_cpus define the parallelization;zero_guard is the small positive number added to zero cells in the sample for MDE estimation;degrees is a list of tuples that define the degrees of the polynomials for the basis functions; e.g. an (a,b) tuple means that the basis function is $L_a(x)L_b(y)$, where $x$ and $y$ are the ages of the partners and $L_a$ is the Legendre polynomial of degree $a$. In addition to these terms, the basis functions also include the constant term; $\mathbf{1}(x>y)$, and a term proportional to $\max(x-y,0)$. The function generate_bases in specification.py creates the basis functions.
Questions
Please direct all questions to Bernard Salanie.
