  Three-dimensional panel models are widely used in empirical analysis.
Researchers use various combinations of fixed effects for three-dimensional
panels. When one imposes a parsimonious model and the true model is rich, then
it incurs mis-specification biases. When one employs a rich model and the true
model is parsimonious, then it incurs larger standard errors than necessary. It
is therefore useful for researchers to know correct models. In this light, Lu,
Miao, and Su (2018) propose methods of model selection. We advance this
literature by proposing a method of post-selection inference for regression
parameters. Despite our use of the lasso technique as means of model selection,
our assumptions allow for many and even all fixed effects to be nonzero.
Simulation studies demonstrate that the proposed method is more precise than
under-fitting fixed effect estimators, is more efficient than over-fitting
fixed effect estimators, and allows for as accurate inference as the oracle
estimator.
