# Essays on spatial dynamic panel data model theories and applications /

Abstract (Summary)

This dissertation is composed of three papers about the theories and application of
spatial dynamic panel data model with …xed e¤ects. The …rst paper investigates the
asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic
panel data with …xed e¤ects when both the number of individuals n and the number
of time periods T are large. We consider the case where T is asymptotically large
relative to n, the case where T is asymptotically proportional to n, and the case
where n is asymptotically large relative to T . In the case where T is asymptotically
large relative to n, the estimators are pnT consistent and asymptotically normal,
with the limit distribution centered around 0. When n is asymptotically proportional
to T , the estimators are pnT consistent and asymptotically normal, but the limit
distribution is not centered around 0; and when n is large relative to T , the estimators
are consistent with rate T , and have a degenerate limit distribution. We also propose
a bias correction for our estimators. We show that when T grows faster than n1=3,
the correction will asymptotically eliminate the bias and yield a centered con…dence
interval. The second paper covers a nonstationary case where there are units roots
in the data generating process. When not all the roots in the DGP are unity, the
estimators’ rate of convergence will be the same as the stationary case, and the
estimators can be asymptotically normal. But for the estimators’asymptotic variance
matrix, it will be driven by the nonstationary component into a singular matrix.
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Consequently, a linear combination of the spatial and dynamic e¤ects can converge
with a higher rate. We also propose a bias correction for our estimators. We show
that when T grows faster than n1=3, the correction will asymptotically eliminate the
bias and yield a centered con…dence interval. In the third paper, a spatial dynamic
panel data approach is proposed to study growth convergence in the U.S. economy. In
neoclassical model, countries are assumed to be independent from each other, which
does not hold in the real world. We introduce technological spillovers and factor
mobility into the neoclassical framework, showing that the convergence rate is higher
and there is spatial correlation. Exploiting annual data on personal state income
spanning period 1961-2000 for the 48 contiguous states, we obtain empirical results
consistent with the model prediction.
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Bibliographical Information:

Advisor:

School:The Ohio State University

School Location:USA - Ohio

Source Type:Master's Thesis

Keywords:econometrics estimation theory spatial analysis statistics

ISBN:

Date of Publication: