FixedEffectModelPyHDFE 0.0.3

Solutions to linear model with high dimensional fixed effects.

FixedEffectModelPyHDFE: A Python Package for Linear Model with High Dimensional Fixed Effects.

FixedEffectModel is a Python Package designed and built by Kuaishou DA ecology group. It provides solutions for linear model with high dimensional fixed effects,including support for calculation in variance (robust variance and multi-way cluster variance), fixed effects, and standard error of fixed effects. It also supports model with instrument variables (will upgrade in late Nov.2020).

As You may have noticed, this is not FixedEffectModel, but rather FixedEffectModelPyHDFE. In this version, the fixed effects backend was switched to use the PyHDFE library, offering significant speed increases with no downsides.

Installation

Install this package directly from PyPI

$ pip install FixedEffectModelPyHDFE

Main Functions

Function name	Description	Usage
ols_high_d_category	get main result	ols_high_d_category(data_df, consist_input=None, out_input=None, category_input=None, cluster_input=[],fake_x_input=[], iv_col_input=[], formula=None, robust=False, c_method='cgm', psdef=True, epsilon=1e-8, max_iter=1e6, process=5)
ols_high_d_category_multi_results	get results of multiple models based on same dataset	ols_high_d_category_multi_results(data_df, models, table_header)
getfe	get fixed effects	getfe(result, epsilon=1e-8)
alpha_std	get standard error of fixed effects	alpha_std(result, formula, sample_num=100)

Example

import FixedEffectModelPyHDFE.api as FEM
import pandas as pd

df = pd.read_csv('path/to/yourdata.csv')

#define model
#you can define the model through defining formula like 'dependent variable ~ continuous variable|fixed_effect|clusters|(endogenous variables ~ instrument variables)'
formula_without_iv = 'y~x+x2|id+firm|id+firm'
formula_without_cluster = 'y~x+x2|id+firm|0|(Q|W~x3+x4+x5)'
formula = 'y~x+x2|id+firm|id+firm|(Q|W~x3+x4+x5)'
result1 = FEM.ols_high_d_category(df, formula = formula,robust=False,c_method = 'cgm',epsilon = 1e-8,psdef= True,max_iter = 1e6)

#or you can define the model through defining each part
consist_input = ['x','x2']
output_input = ['y']
category_input = ['id','firm']
cluster_input = ['id','firm']
endo_input = ['Q','W']
iv_input = ['x3','x4','x5']
result1 = FEM.ols_high_d_category(df,consist_input,out_input,category_input,cluster_input,endo_input,iv_input,formula=None,robust=False,c_method = 'cgm',epsilon = 1e-8,max_iter = 1e6)

#show result
result1.summary()

#get fixed effects
getfe(result1 , epsilon=1e-8)

#define the expression of standard error of difference between two fixed effect estimations you want to know
expression = 'id_1-id_2'
#get standard error
alpha_std(result1, formula = expression , sample_num=100)

Requirements

• Python 3.6+
• Pandas and its dependencies (Numpy, etc.)
• Scipy and its dependencies
• statsmodels and its dependencies
• networkx

Citation

If you use FixedEffectModel in your research, please cite us as follows:

Kuaishou DA Ecology. FixedEffectModel: A Python Package for Linear Model with High Dimensional Fixed Effects.https://github.com/ksecology/FixedEffectModel,2020.Version 0.x

BibTex:

@misc{FixedEffectModel,
  author={Kuaishou DA Ecology},
  title={{FixedEffectModel: {A Python Package for Linear Model with High Dimensional Fixed Effects}},
  howpublished={https://github.com/ksecology/FixedEffectModel},
  note={Version 0.x},
  year={2020}
}

Feedback

This package welcomes feedback. If you have any additional questions or comments, please contact [e-mail].

Reference

[1] Simen Gaure(2019). lfe: Linear Group Fixed Effects. R package. version:v2.8-5.1 URL:https://www.rdocumentation.org/packages/lfe/versions/2.8-5.1

[2] A Colin Cameron and Douglas L Miller. A practitioner’s guide to cluster-robust inference. Journal of human resources, 50(2):317–372, 2015.

[3] Simen Gaure. Ols with multiple high dimensional category variables. Computational Statistics & Data Analysis, 66:8–18, 2013.

[4] Douglas L Miller, A Colin Cameron, and Jonah Gelbach. Robust inference with multi-way clustering. Technical report, Working Paper, 2009.

[5] Jeffrey M Wooldridge. Econometric analysis of cross section and panel data. MIT press, 2010.