This paper studies the estimation of a dynamic spatial panel data model with interactive
individual and time effects with large n and T. The model has a rich spatial structure
including contemporaneous spatial interaction and spatial heterogeneity. Dynamic features
include individual time lag and spatial diffusion. The interactive effects capture
heterogeneous impacts of time effects on cross sectional units. The interactive effects are
treated as parameters, so as to allow correlations between the interactive effects and the …

We propose an M-estimation method for estimating the spatial dynamic panel data models
with interactive fixed effects based on short panels. Unbiased estimating function (EF) is
obtained by adjusting the concentrated conditional quasi score, given initial values and with
factor loadings being concentrated out, to account for the effects of conditioning and
concentration. For inference, the EF is decomposed into a sum of n nearly uncorrelated
terms. Average of outer products of these n terms together with a covariance adjustment …