SF4wD

four-component stochastic frontier model with determinants

Motivation

This package was developed to complement four-component stochastic frontier that consider

determinants in mean and variance parameters of inefficiency distributions

by Ruei-Chi Lee.

Features

• SF4wD: main.py - set method and model to run simulation or real data

• HMC: Hamilton Monte Carlo designed for determinants parameters.

• DA: Data augmentation for the model

• TK: Two-parametrization method originally proposed by Tsiona and Kunmbhaker (2014) for four-component model without determinants.

• PMCMC: Particle MCMC for the model (perferred approach) - speed up by GPU parallel computation

License

Ruei-Chi Lee is the main author and contributor.

Bug reports, feature requests, questions, rants, etc are welcome, preferably on the github page.