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Testing for heteroskedasticity in two-way fixed effects panel data models.

Sanying Feng1, Gaorong Li2, Tiejun Tong3

  • 1School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, People's Republic of China.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

We introduce a novel method for testing heteroskedasticity in two-way fixed effects panel data models. This new approach is distribution-free and effective even with large datasets, offering practical applications in econometrics.

Keywords:
Asymptotic distributionheteroskedasticitypanel datatestingtwo-way fixed effects model

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Area of Science:

  • Econometrics
  • Statistical Inference

Background:

  • Panel data models with two-way fixed effects are widely used in econometrics.
  • Testing for heteroskedasticity is crucial for valid inference in these models.
  • Existing methods may face limitations with large cross-sectional or temporal dimensions.

Purpose of the Study:

  • To propose a new, robust method for testing heteroskedasticity in two-way fixed effects panel data models.
  • To develop test statistics applicable to scenarios with large cross-sectional dimensions and either large or fixed temporal dimensions.
  • To provide distribution-free tests that are easy to implement.

Main Methods:

  • Development of test statistics within the conditional moment framework.
  • Derivation of asymptotic distributions under null and alternative hypotheses.
  • Utilizing simple auxiliary regressions for implementation.

Main Results:

  • The proposed tests are distribution-free.
  • The tests are shown to perform well in simulation studies.
  • Real data analyses confirm the practical utility of the new tests.

Conclusions:

  • The developed method provides a valuable tool for heteroskedasticity testing in challenging panel data settings.
  • The tests are computationally simple and statistically sound.
  • The approach has potential for broad application in econometric analysis with panel data.