samplefit 0.3.1

samplefit library implements the Sample Fit Reliability algorithm for the assessment of sample fit in econometric models.

samplefit

samplefit is a Python library to assess sample fit, as opposed to model fit, via the Sample Fit Reliability algorithm as developed by Okasa & Younge (2022). samplefit is linked to the statsmodels library (Seabold & Perktold, 2010) and follows the same command workflow.

Copyright (c) 2022 Gabriel Okasa & Kenneth A. Younge.

AUTHOR:  Gabriel Okasa & Kenneth A. Younge
SOURCE:  https://github.com/okasag/samplefit
LICENSE: Access to this code is provided under an MIT License.

Repo maintainer: Gabriel Okasa ([e-mail])

Introduction

samplefit is a Python library for the assessment of sample fit in econometric models. In particular, samplefit implements the Sample Fit Reliability (SFR) algorithm, a re-sampling procedure to estimate the reliability of data and check the sensitivity of results. To that end, SFR is a computational approach with three aspects: Scoring, to estimate a point-wise reliability score for every observation in a sample based on the expected estimation loss over sub-samples; Annealing, to test the sensitivity of results to the sequential removal of unreliable data points; and Fitting, to estimate a weighted regression that adjusts for the reliability of the data.

Detailed documentation of the samplefit library is available here.

Installation

To install the samplefit library from PyPi run:

pip install samplefit

or alternatively, to clone the repo run:

git clone https://github.com/okasag/samplefit.git

The required modules can be installed by navigating to the root of the cloned project and executing the following command: pip install -r requirements.txt.

Example

The example below demonstrates the workflow of using the samplefit library in conjunction with the well-known statsmodels library.

Import libraries:

import samplefit as sf
import statsmodels.api as sm

Get data:

boston = sm.datasets.get_rdataset("Boston", "MASS")
Y = boston.data['crim']
X = boston.data['lstat']
X = sm.add_constant(X)

Assess model fit:

model = sm.OLS(endog=Y, exog=X)
model_fit = model.fit()
model_fit.summary()

Assess sample fit:

sample = sf.SFR(linear_model=model)
sample_fit = sample.fit()
sample_fit.summary()

Assess sample reliability:

sample_scores = sample.score()
sample_scores.plot()

Assess sample sensitivity:

sample_annealing = sample.anneal()
sample_annealing.plot()

References

• Okasa, Gabriel, and Kenneth A. Younge. “Sample Fit.” Working Paper. 2022.
• Seabold, Skipper, and Josef Perktold. “statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference. 2010.