An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects

George Kapetanios; Laura Serlenga; Yongcheol Shin

doi:10.1080/07350015.2023.2238774

An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects

George Kapetanios^*, Laura Serlenga, Yongcheol Shin

^*Corresponding author for this work

Banking & Finance

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

A huge literature on modeling cross-sectional dependence in panels has been developed using interactive effects (IE). One area of contention is the hypothesis concerned with whether the regressors and factor loadings are correlated or not. Under the null hypothesis that they are conditionally independent, we can still apply the consistent and robust two-way fixed effects estimator. As an important specification test we develop an LM test for both static and dynamic panels with IE. Simulation results confirm the satisfactory performance of the LM test in small samples. We demonstrate its usefulness with an application to a total of 22 datasets, including static panels with a small T and dynamic panels with serially correlated factors, providing convincing evidence that the null hypothesis is not rejected in.

Original language	English
Pages (from-to)	743-761
Number of pages	19
Journal	Journal of Business and Economic Statistics
Volume	42
Issue number	2
DOIs	https://doi.org/10.1080/07350015.2023.2238774
Publication status	Published - 2024

Keywords

Conditional independence between the regressors and factor loadings
Panel data model with interactive effects
The LM test

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10.1080/07350015.2023.2238774

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abstract = "A huge literature on modeling cross-sectional dependence in panels has been developed using interactive effects (IE). One area of contention is the hypothesis concerned with whether the regressors and factor loadings are correlated or not. Under the null hypothesis that they are conditionally independent, we can still apply the consistent and robust two-way fixed effects estimator. As an important specification test we develop an LM test for both static and dynamic panels with IE. Simulation results confirm the satisfactory performance of the LM test in small samples. We demonstrate its usefulness with an application to a total of 22 datasets, including static panels with a small T and dynamic panels with serially correlated factors, providing convincing evidence that the null hypothesis is not rejected in.",

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AU - Kapetanios, George

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AU - Shin, Yongcheol

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AB - A huge literature on modeling cross-sectional dependence in panels has been developed using interactive effects (IE). One area of contention is the hypothesis concerned with whether the regressors and factor loadings are correlated or not. Under the null hypothesis that they are conditionally independent, we can still apply the consistent and robust two-way fixed effects estimator. As an important specification test we develop an LM test for both static and dynamic panels with IE. Simulation results confirm the satisfactory performance of the LM test in small samples. We demonstrate its usefulness with an application to a total of 22 datasets, including static panels with a small T and dynamic panels with serially correlated factors, providing convincing evidence that the null hypothesis is not rejected in.

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An LM Test for the Conditional Independence between Regressors and Factor Loadings in Panel Data Models with Interactive Effects

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Keywords

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