Wenyang Zhang - (University of York)

Homogeneity Pursuit in Single Index Models based Panel Data Analysis

Panel data analysis is an important topic in statistics and econometrics.
Traditionally, in panel data analysis, all individuals are assumed to share the
same unknown parameters, e.g. the same coefficients of covariates when the
linear models are used, and the differences between the individuals are
accounted for by cluster effects. This kind of modelling only makes sense if
our main interest is on the global trend, this is because it would not be able
to tell us anything about the individual attributes which are sometimes very
important. In this talk, I will present a new modelling approach, based on the
single index models embedded with homogeneity, for panel data analysis, which
builds the individual attributes in the model and is parsimonious at the same
time. I will show a data driven approach to identify the structure of
homogeneity, and estimate the unknown parameters and functions based on the
identified structure. I will show the asymptotic properties of the resulting
estimators. I will also use intensive simulation studies to show how well
the resulting estimators work when sample size is finite. Finally, I will
apply the proposed modelling idea to a public financial dataset and a UK
climate dataset, and show some interesting findings.