Nonparametric Estimation of the Random Coefficients Model in Python

Abstract

We present $\text{PyRMLE}$, a Python module that implements Regularized Maximum Likelihood Estimation for the analysis of Random Coefficient models. $\text{PyRMLE}$ is simple to use and readily works with data formats that are typical to Random Coefficient problems. The module makes use of Python's scientific libraries $\text{NumPy}$ and $\text{SciPy}$ for computational efficiency. The main implementation of the algorithm is executed purely in Python code which takes advantage of Python's high-level features.

30 pages, 22 figures