  Lancaster (2002} proposes an estimator for the dynamic panel data model with
homoskedastic errors and zero initial conditions. In this paper, we show this
estimator is invariant to orthogonal transformations, but is inefficient
because it ignores additional information available in the data. The zero
initial condition is trivially satisfied by subtracting initial observations
from the data. We show that differencing out the data further erodes efficiency
compared to drawing inference conditional on the first observations.
  Finally, we compare the conditional method with standard random effects
approaches for unobserved data. Standard approaches implicitly rely on normal
approximations, which may not be reliable when unobserved data is very skewed
with some mass at zero values. For example, panel data on firms naturally
depend on the first period in which the firm enters on a new state. It seems
unreasonable then to assume that the process determining unobserved data is
known or stationary. We can instead make inference on structural parameters by
conditioning on the initial observations.
