Estimate two way fixed effect labor models
Project description
pytwoway
Two way fixed effect models for labor in python
Full documentation can be found here.
Quick start:
To install from pip:
pip install pytwoway
To run using command line interface:
pytw --my-config config.txt --akm --cre
Example config.txt:
data = file.csv
filetype = csv
col_dict = "{'fid': 'your_firmid_col', 'wid': 'your_workerid_col', 'year': 'your_year_col', 'comp': 'your_compensation_col'}"
To run in Python:
- If you have data
from pytwoway import twfe_network
tn = twfe_network.twfe_network
# Create twfe object
tw_net = tn.twfe_network(data, formatting, col_dict)
# Convert long data into event study data (not necessary if the data is already in event study format):
tw_net.refactor_es()
# Run the bias-corrected AKM estimator:
tw_net.run_akm_corrected(user_akm)
# Cluster firms based on their wage CDFs (required for the CRE estimator)
tw_net.cluster(user_cluster)
# Run the CRE estimator
tw_net.run_cre(user_cre)
- If you want to simulate data
from pytwoway import sim_twfe_network
sn = sim_twfe_network.sim_twfe_network
# Create simulated twfe object
stw_net = sn(sim_params)
# Generate data
sim_data = stw_net.sim_network()
- If you want to run Monte Carlo on simulated data
from pytwoway import sim_twfe_network
sn = sim_twfe_network.sim_twfe_network
# Create simulated twfe object
stw_net = sn(sim_params)
# Run Monte Carlo
stw_net.twfe_monte_carlo(N, ncore, akm_params, cre_params, cluster_params)
# Plot results
stw_net.plot_monte_carlo()
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