Three Essays on Econometrics

The first chapter of this dissertation introduces
the generalized minimum contrast estimator (GMC)
with
nonsmooth moment functions and studies its
asymptotic properties under mild conditions. A Chi-
square test based on GMC is discussed. The duality
between the GMC and the Generalized Empirical
Likelihood Estimator (GEL) is interpreted from both
a computational perspective and geometric
perspective. The second chapter investigates a
Bayesian approach to calculating the GMC. By
updating the concerned parameters and nuisance
parameters alternatively, this approach can converge
quickly and can be implemented easily. Its
performance is compared to existing methods based on
both just-identified conditions and over-identified
conditions. The third chapter employs a partial
linear model (PL) and geographically weighted
regression (GWR) to estimate housing prices. These
two semiparametric models provide flexible
consideration of spatial heterogeneity and spatial
correlation. Their performance is compared through
various simulation experiments. An empirical study
of the housing market in Connecticut is also
implemented.

Autorentext

Zhiwei Ma received his B.S. and M.A. from Tsinghua University in China. He also received his M.S. in Mathematics and Ph.D in Economics from University of Connecticut. In June 2007, Zhiwei joined the State Street Corporation in Boston. He is currently a vice president in the Risk Analytics team of Enterprise Risk Management group.

Klappentext

The first chapter of this dissertation introduces the generalized minimum contrast estimator (GMC) with nonsmooth moment functions and studies its asymptotic properties under mild conditions. A Chi-square test based on GMC is discussed. The duality between the GMC and the Generalized Empirical Likelihood Estimator (GEL) is interpreted from both a computational perspective and geometric perspective. The second chapter investigates a Bayesian approach to calculating the GMC. By updating the concerned parameters and nuisance parameters alternatively, this approach can converge quickly and can be implemented easily. Its performance is compared to existing methods based on both just-identified conditions and over-identified conditions. The third chapter employs a partial linear model (PL) and geographically weighted regression (GWR) to estimate housing prices. These two semiparametric models provide flexible consideration of spatial heterogeneity and spatial correlation. Their performance is compared through various simulation experiments. An empirical study of the housing market in Connecticut is also implemented.