fixedeffectmodel's Introduction

FixedEffectModel

This is a Python Package that 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.

Installation

Install this package directly from PyPI

$ pip install FixedEffectModel

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=[],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 FixedEffectModel.api as FEM
import pandas as pd

df = pd.read_csv('yourdata.csv')

#define model:'dependent variable ~ continuous variable|fixed_effect|clusters'
formula = 'y~x+x2|id+firm|id+firm'
result1 = FEM.ols_high_d_category(df, formula = formula,robust=False,c_method = 'cgm',epsilon = 1e-8,psdef= True,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)