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Robustness Checks for Panel Data Fixed Effects model
Robustness Checks for Panel Data Fixed Effects model

I am using the following fixed effects model:

Log(CEO Compensation_i,t) = b₁Firmperformance_i,t-1+ b₂Proportionofnonexecutivedirectors_i,t + b₃CEO Tenure_i,t+ b₄CEOage_i,t+ b₅Log(CEOshareholdings_i,t) + b₆BoardSize_i,t+ b₇Log(FirmSize_i,t) + Time dummy + 𝜀_{𝑖𝑡

I include a robust clustered standard errors in the fixed effects model. This is my first paper and I want to assess the the robustness of my results. I have already} already accounted for heteroskedasticity and/or autocorrelation via clustered standard errors. However I suspect endogeneity from the firm performance variable which is already lagged as according to literature this helps control it. How do I test for this, do I test for reverse causality? Is it simple to use a GMM model? Is it simple to do so? Please let me know any robustness checks I could use. Many thanks

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