Dear all,

I would like to know more about the relation between serial correlation/autocorrelation and static vs. dynamic panel data models to decide between a static or dynamic model.

Currently, I am analyzing an unbalanced panel data set with individual and time fixed effects. Based on the theory, I do not have strong reasons to apply dynamic panel models (e.g. lagged dependent variable / Arellano-Bond estimator). However, I would like to exclude the possibility of misspecification/inconsistency/ inefficiency.

Because serial correlation in linear panel-data models biases the standard errors and causes the results to be less efficient, researchers need to identify serial correlation in the idiosyncratic error term in a panel-data model. If serial correlation is present, the model is inefficient and I need to correct a least my standard error that is robust to serial correlation.
  • If serial correlation IS present, under which circumstances is a dynamic panel model (e.g. Arellano-Bond estimator) more appropriate than just correcting the standard errors?
  • If serial correlation IS NOT present, does this confirm my assumption of static panel data?
Correcting the standard error will increase efficiency. What is about consistency?
  • What are statistical reasons to add the lagged dependent variable?
  • If I add the lagged dependent variable to my fixed-effect model and the coefficient does/does not get significant, what does it tell me?