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Related Experiment Video

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Estimating group fixed effects in panel data with a binary dependent variable: How the LPM outperforms logistic

Joan C Timoneda1

  • 1Purdue University Department of Political Science 2230 Beering Hall 100 University St. West Lafayette, IN, 47907, USA.

Social Science Research
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

For rare events data, the Linear Probability Model (LPM) with fixed effects offers more accurate estimates than maximum likelihood methods when ones are infrequent. This study provides guidance on choosing fixed effects models for time-series cross-sectional data.

Keywords:
Fixed effectsLinear probability modelMaximum likelihoodRare eventsTime-series cross-sectional data

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Area of Science:

  • Econometrics
  • Statistical Modeling
  • Social Sciences

Background:

  • Estimating fixed effects models with rare events data presents significant challenges.
  • Researchers often grapple with trade-offs between the Linear Probability Model (LPM), logistic regression with group intercepts, and conditional logit models.

Purpose of the Study:

  • To survey the trade-offs in selecting fixed effects models for rare events data.
  • To argue for the superiority of the LPM with fixed effects under specific conditions.
  • To provide a novel technique for model selection based on event frequency.

Main Methods:

  • Survey of existing econometric models for rare events data.
  • Monte Carlo simulations to compare model performance.
  • Analysis of time-series cross-sectional (TSCS) data structures and big data.

Main Results:

  • The Linear Probability Model (LPM) with fixed effects yields more accurate estimates and predicted probabilities than maximum likelihood specifications when the dependent variable has less than 25% ones.
  • Simulation results demonstrate specific conditions favoring the LPM with fixed effects.

Conclusions:

  • The LPM with fixed effects is a preferred method for rare events data when the prevalence of the outcome is low (less than 25%).
  • This research offers clarity on fixed effects model selection in TSCS data based on event frequency.