Empirical Likelihood in Statsmodels

by Justin Grana for Python Software Foundation

In 1990, Art Owen published “Empirical Likelihood Ratio Confidence Regions” in The Annals of Statistics, which ignited a fury of research exploring the techniques and possibilities of empirical likelihood estimation. In 2009, statsmodels was released as its own package for statistical computing in the Python language and has subsequently grown to include among others, linear, nonlinear and time series regression models. In a way, empirical likelihood estimation and statsmodels are similar; they are both relatively new in their respective fields but are packed with unexploited opportunities that can benefit researchers, financial analysts and policymakers alike. That is why I propose implementing empirical likelihood estimation in statsmodels for my Google Summer of Code 2012 project.