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 $\root \of {nT}$ consistent and asymptotically centered normal; when n is asymptotically proportional to T , they are $\root \of {nT}$ 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 n 1/3 / 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.
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