Conference Agenda

When panel data exhibit cross-sectional dependence, particular care is required, as cross-dependence may be induced by omitting relevant variables. If these variables correlate with the regressors, rendering them endogenous, sophisticated approaches such as the CCE approach or the PC estimator are recommended. These approaches may however be difficult to implement or build on strong assumptions. Therefore, if regressor endogeneity can reasonably be excluded, it is common to resort to simpler estimators in conjunction with panel-robust standard errors.
In practice, it is furthermore not uncommon for cross-dependence to be of various strength, ranging from weak (e.g.\ in spatial data models) to strong (when unobserved factors load onto all units).
We quantify the impact the strength of cross-dependence has on the within estimator and on the associated (panel-robust) tests when unobserved factors in errors are not confounding. Using theoretical justifications and Monte Carlo simulation we find that cross-dependence alone in the errors is not sufficient to impact the fixed-effects estimator or its standard errors. Rather, it is the interaction of error and regressor cross-dependence that causes problems such as reduced convergence rates and distorted significance tests in finite samples.