# Stochastic Frontier Analysis (SFA)
## Installation
The [`pySFA`](https://pypi.org/project/pysfa/) package is now avaiable on PyPI and the latest development version can be installed from the Github repository [`pySFA`](https://github.com/gEAPA/pySFA). Please feel free to download and test it. We welcome any bug reports and feedback.
#### PyPI [](https://pypi.org/project/pysfa/)[](https://pypistats.org/packages/pysfa)
pip install pysfa
#### GitHub
pip install -U git+https://github.com/gEAPA/pySFA
## Authors
- [Sheng Dai](https://daisheng.io), PhD, Turku School of Economics, University of Turku, Finland.
- [Zhiqiang Liao](https://liaozhiqiang.com), Doctoral Researcher, Aalto University School of Business, Finland.
## Demo: Estimating a production function by `pySFA`
```python
import numpy as np
import pandas as pd
from pysfa import SFA
from pysfa.dataset import load_Tim_Coelli_frontier
# import the data from Tim Coelli Frontier 4.1
df = load_Tim_Coelli_frontier(x_select=['labour', 'capital'],
y_select=['output'])
y = np.log(df.y)
x = np.log(df.x)
# Estimate SFA model
res = SFA.SFA(y, x, fun=SFA.FUN_PROD, method=SFA.TE_teJ)
res.optimize()
# print estimates
print(res.get_beta())
print(res.get_residuals())
# print estimated parameters
print(res.get_lambda())
print(res.get_sigma2())
print(res.get_sigmau2())
print(res.get_sigmav2())
# print statistics
print(res.get_pvalue())
print(res.get_tvalue())
print(res.get_std_err())
# OR print summary
print(res.summary())
# print TE
print(res.get_technical_efficiency())
```
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"description": "# Stochastic Frontier Analysis (SFA)\n\n## Installation\n\nThe [`pySFA`](https://pypi.org/project/pysfa/) package is now avaiable on PyPI and the latest development version can be installed from the Github repository [`pySFA`](https://github.com/gEAPA/pySFA). Please feel free to download and test it. We welcome any bug reports and feedback.\n\n#### PyPI [](https://pypi.org/project/pysfa/)[](https://pypistats.org/packages/pysfa)\n\n pip install pysfa\n\n#### GitHub\n\n pip install -U git+https://github.com/gEAPA/pySFA\n\n## Authors\n\n- [Sheng Dai](https://daisheng.io), PhD, Turku School of Economics, University of Turku, Finland.\n- [Zhiqiang Liao](https://liaozhiqiang.com), Doctoral Researcher, Aalto University School of Business, Finland.\n\n## Demo: Estimating a production function by `pySFA`\n\n```python\nimport numpy as np\nimport pandas as pd\nfrom pysfa import SFA\nfrom pysfa.dataset import load_Tim_Coelli_frontier\n\n\n# import the data from Tim Coelli Frontier 4.1\ndf = load_Tim_Coelli_frontier(x_select=['labour', 'capital'],\n y_select=['output'])\ny = np.log(df.y)\nx = np.log(df.x)\n\n# Estimate SFA model\nres = SFA.SFA(y, x, fun=SFA.FUN_PROD, method=SFA.TE_teJ)\nres.optimize()\n# print estimates\nprint(res.get_beta())\nprint(res.get_residuals())\n\n# print estimated parameters\nprint(res.get_lambda())\nprint(res.get_sigma2())\nprint(res.get_sigmau2())\nprint(res.get_sigmav2())\n\n# print statistics\nprint(res.get_pvalue())\nprint(res.get_tvalue())\nprint(res.get_std_err())\n\n# OR print summary\nprint(res.summary())\n\n# print TE\nprint(res.get_technical_efficiency())\n```\n",
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