sw2023 — Nonparametric Multiple-Output Stochastic Frontier Analysis

sw2023 implements the nonparametric stochastic frontier estimator of Simar & Wilson (2023, Journal of Business & Economic Statistics) for production technologies with multiple inputs and multiple outputs.

pip install sw2023

User Guide

• Installation
  • Requirements
  • From PyPI
  • From Source
  • Stata Integration
• Quick Start
  • Cross-Sectional Model
  • Asymptotic Confidence Intervals
  • Bootstrap Confidence Intervals
  • Wild Bootstrap Significance Test
  • Diagnostic Plots
  • Panel Model (4-Component)
• Methodology
  • The SW(2023) Estimator
  • Rotation Transform
  • Conditional Moment Estimation (LLLS)
  • Inefficiency Estimation
  • JLMS Efficiency
  • 4-Component Panel Extension
  • References

API Reference

• Cross-Sectional Model
  • SW2023Model
• Panel Model
  • PanelSW2023
• Result Classes
  • ConfintResult
  • BootstrapResult
  • SignificanceTestResult
• Bootstrap Functions
  • bootstrap_sw()
  • bootstrap_panel()
  • test_r3_significance()
• Visualization
  • Cross-Sectional
  • Panel
• Bandwidth Selection
  • bandwidth_loocv_product()
  • bandwidth_loocv()
  • bandwidth_silverman()

More

• Replication
• Changelog

References

Simar, L. & Wilson, P.W. (2023). Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs. Journal of Business & Economic Statistics, 41(4), 1391–1403. https://doi.org/10.1080/07350015.2022.2110882

Parmeter, C.F., Simar, L., Van Keilegom, I. & Zelenyuk, V. (2024). Inference in the Nonparametric Stochastic Frontier Model. Econometric Reviews, 43(7), 518–539. https://doi.org/10.1080/07474938.2024.2339193