A. It increases the model's goodness-of-fit. B. It leads to biased estimates of the regression coefficients. C. It causes the standard errors of the coefficients to be inflated. D. It violates the normality assumption of the error terms.

A. When the number of cross-sectional units is larger than the number of time periods. B. When the individual effects are correlated with the explanatory variables. C. When there is no heterogeneity across cross-sectional units. D. When the data set is balanced.