Arbeitspapier

Fixed Effects and Causal Inference

Across many disciplines, the fixed effects estimator of linear panel data models is the default method to estimate causal effects with nonexperimental data that are not confounded by time-invariant, unit-specific heterogeneity. One feature of the fixed effects estimator, however, is often overlooked in practice: With data over time t ∈ {1,...,T} for each unit of observation i ∈ {1,...,N}, the amount of unobserved heterogeneity the researcher can remove with unit fixed effects is weakly decreasing in T. Put differently, the set of attributes that are time-invariant is not invariant to the length of the panel. We consider several alternatives to the fixed effects estimator with T > 2 when relevant unit-specific heterogeneity is not time-invariant, including existing estimators such as the first-difference, twice first-differenced, and interactive fixed effects estimators. We also introduce several novel algorithms based on rolling estimators. In the situations considered here, there is little to be gained and much to lose by using the fixed effects estimator. We recommend reporting the results from multiple linear panel data estimators in applied research.

Language
Englisch

Bibliographic citation
Series: IZA Discussion Papers ; No. 16202

Classification
Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Subject
panel data
fixed effects
first-differences
interactive fixed effects
unobserved heterogeneity
time-varying individual effects

Event
Geistige Schöpfung
(who)
Millimet, Daniel L.
Bellemare, Marc
Event
Veröffentlichung
(who)
Institute of Labor Economics (IZA)
(where)
Bonn
(when)
2023

Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Millimet, Daniel L.
  • Bellemare, Marc
  • Institute of Labor Economics (IZA)

Time of origin

  • 2023

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