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Co-authored-by: jose-donato <zmcdonato@gmail.com>
Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com>
Co-authored-by: andrewkenreich <andrew.kenreich@gmail.com>
2023-10-30 21:01:29 +00:00

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title, description, keywords
title description keywords
panel The documentation page provides a comprehensive guide to performing regression analysis on Panel Data using the 'panel' command in Python. It includes information about the usage, command parameters and the type of regression analysis you can perform.
panel data regression
data analysis
regression analysis
Pooled OLS
Random Effects
Between OLS
Fixed Effects
First Difference OLS
entity effects
time effects

import HeadTitle from '@site/src/components/General/HeadTitle.tsx';

Performs regression analysis on Panel Data. There are a multitude of options to select from to fit the needs of restrictions of the dataset.

Usage

panel -d DEPENDENT -i INDEPENDENT [-r {pols,re,bols,fe,fdols,POLS,RE,BOLS,FE,FDOLS}] [-e] [-t]

Parameters

Name Description Default Optional Choices
dependent The dependent variable on the regression you would like to perform None False None
independent The independent variables on the regression you would like to perform. E.g. wage_panel.married,wage_panel.union None False None
type The type of regression you wish to perform. This can be either pols (Pooled OLS), re (Random Effects), bols (Between OLS), fe (Fixed Effects) or fdols (First Difference OLS) pols True pols, re, bols, fe, fdols, POLS, RE, BOLS, FE, FDOLS
entity_effects Using this command creates entity effects, which is equivalent to including dummies for each entity. This is only used within Fixed Effects estimations (when type is set to 'fe') False True None
time_effects Using this command creates time effects, which is equivalent to including dummies for each time. This is only used within Fixed Effects estimations (when type is set to 'fe') False True None