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This study introduces a classical minimum distance (CMD) estimator for linear fixed-effects models with missing data. This method improves efficiency by utilizing all covariate information, even when dependent variables are missing.

Keywords:
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Area of Science:

  • Econometrics
  • Statistics

Background:

  • Linear fixed-effects models are widely used but can be challenging when data is missing.
  • Traditional methods may lose efficiency with missing dependent variables.

Purpose of the Study:

  • To propose a novel estimation method for linear fixed-effects models with missing dependent variables.
  • To enhance estimation efficiency by leveraging all available covariate data.

Main Methods:

  • A classical minimum distance (CMD) estimator, building on Chamberlain's work, is developed.
  • The estimator is shown to be consistent under missing-at-random (MAR) assumptions.
  • The approach is extended to autoregressive fixed-effects models with lagged dependent variables.

Main Results:

  • The CMD estimator offers efficiency gains compared to complete-data methods.
  • Identification of model parameters is possible even without 'within' variation in certain cases.
  • Monte Carlo simulations demonstrate the CMD approach's performance against existing methods.

Conclusions:

  • The CMD estimator provides a robust and efficient solution for fixed-effects models with missing data.
  • The method is applicable to static and dynamic (autoregressive) models.
  • Further extensions address sequential exogeneity and missing covariates.