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

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  • Sanying Feng
  • Gaorong Li
  • Tiejun Tong
  • Shuanghua Luo

Abstract

In this paper, we propose a new method for testing heteroskedasticity in two-way fixed effects panel data models under two important scenarios where the cross-sectional dimension is large and the temporal dimension is either large or fixed. Specifically, we will develop test statistics for both cases under the conditional moment framework, and derive their asymptotic distributions under both the null and alternative hypotheses. The proposed tests are distribution free and can easily be implemented using the simple auxiliary regressions. Simulation studies and two real data analyses demonstrate that our proposed tests perform well in practice, and may have the potential for wide application in econometric models with panel data.

Suggested Citation

  • Sanying Feng & Gaorong Li & Tiejun Tong & Shuanghua Luo, 2020. "Testing for heteroskedasticity in two-way fixed effects panel data models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(1), pages 91-116, January.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:91-116
    DOI: 10.1080/02664763.2019.1634682
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    Cited by:

    1. Sanying Feng & Tiejun Tong & Sung Nok Chiu, 2023. "Statistical Inference for Partially Linear Varying Coefficient Spatial Autoregressive Panel Data Model," Mathematics, MDPI, vol. 11(22), pages 1-19, November.
    2. Kasililika-Mlagha, Emmillian Chifundo, 2021. "The impact of public agriculture expenditure on food security and nutrition in the Southern African Development Community (SADC)," Research Theses 334749, Collaborative Masters Program in Agricultural and Applied Economics.

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