Abstract :
This paper establishes asymptotic properties of quasi-maximum likelihood estimators
for spatial dynamic panel data with both time and individual fixed effects when
the number of individuals n and the number of time periods T can be large. We propose
a data transformation approach to eliminate the time effects. When n/T →0,
the estimators are √nT consistent and asymptotically centered normal; when n is
asymptotically proportional to T , they are √nT consistent and asymptotically normal,
but the limit distribution is not centered around 0; when n/T →∞, the estimators
are consistent with rate T and have a degenerate limit distribution. We
also propose a bias correction for our estimators. When n1/3/T →0, the correction
will asymptotically eliminate the bias and yield a centered confidence interval.
The estimates from the transformation approach can be consistent when n is a fixed
finite number.
