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https://github.com/alexstead/rfrontier

Stata package for robust stochastic frontier analysis

https://github.com/alexstead/rfrontier

Science Score: 26.0%

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Keywords

efficiency-analysis maximum-likelihood-estimation robust-estimation robustness stochastic-frontier-analysis
Last synced: 5 months ago · JSON representation

Repository

Stata package for robust stochastic frontier analysis

Basic Info
  • Host: GitHub
  • Owner: AlexStead
  • License: mit
  • Language: Stata
  • Default Branch: main
  • Homepage:
  • Size: 68.4 KB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 2
Topics
efficiency-analysis maximum-likelihood-estimation robust-estimation robustness stochastic-frontier-analysis
Created almost 4 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

rfrontier

Stata package for robust stochastic frontier analysis

This facilitates estimation of stochastic frontier models with alternative distributional assumptions, including models with in which the noise terms follows a Student's t, Cauchy, or logistic distribution. The package can then be used to generate efficiency predictions, influence functions (for parameter estimates and for efficiency predictions) and related postestimation outputs. The main purposes of this package are to enable easy implementation of some of the stochastic frontier specifications explored in publications I have co-authored, and to ease replication of some of the results reported in these publications.

Installation

In order to install the command, enter the following commands into Stata (the first is unnecessary if you already have the github command installed): stata net install github, from("https://haghish.github.io/github/") github install AlexStead/rfrontier

Getting started

For help with the command's syntax and options, enter (after installation): stata help rfrontier help rfrontier_postestimation

Linked publications

  • Stead AD, Wheat P, and Greene WH. 2023. Robustness in stochastic frontier analysis. In: Macedo P, Moutinho V, and Madaleno M (eds). Advanced Mathematical Methods for Economic Efficiency Analysis. Lecture Notes in Economics and Mathematical Systems. Springer. pp. 197-228. https://doi.org/10.1007/978-3-031-29583-6_12
  • Stead AD, Wheat P, Greene WH. 2023. Robust maximum likelihood estimation of stochastic frontier models. European Journal of Operational Research. 309(1). pp.188-201. https://doi.org/10.1016/j.ejor.2022.12.033
  • Wheat P, Stead AD, Greene WH. 2019. Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England. Journal of Productivity Analysis. 51(1). pp. 21-38. https://doi.org/10.1007/s11123-018-0541-y
  • Stead AD, Wheat P, Greene WH. 2018. Estimating efficiency in the presence of extreme outliers: A logistic-half normal stochastic frontier model with application to highway maintenance costs in England. In: Greene WH, Khalaf L, Makdissi P, Sickles RC, Veall MR, and Voia M-C (eds). Productivity and Inequality. Springer Proceedings in Business and Economics. Springer International Publishing. pp. 1-19. https://doi.org/10.1007/978-3-319-68678-3_1

Owner

  • Name: Dr Alex Stead
  • Login: AlexStead
  • Kind: user
  • Company: University of Leeds

Lecturer in Transport Economics, @ITSLeeds @universityofleeds

GitHub Events

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