Nonparametric Estimation of the Random Coefficients Model in Python

TL;DR

This paper introduces PyRMLE, a Python module that simplifies the estimation of Random Coefficient models using regularized maximum likelihood, leveraging Python's scientific libraries for efficiency.

Contribution

The paper presents PyRMLE, a user-friendly Python package for nonparametric estimation of Random Coefficient models, integrating regularized maximum likelihood with high-level Python features.

Findings

PyRMLE is easy to use with typical data formats.

The module achieves computational efficiency using NumPy and SciPy.

Pure Python implementation enhances accessibility and flexibility.

Abstract

We present $\textbf{PyRMLE}$, a Python module that implements Regularized Maximum Likelihood Estimation for the analysis of Random Coefficient models. $\textbf{PyRMLE}$ 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 $\textbf{NumPy}$ and $\textbf{SciPy}$ for computational efficiency. The main implementation of the algorithm is executed purely in Python code which takes advantage of Python's high-level features.