November 17, 2016 [Department of Mathematics and Statistics, Binghamton University]

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.

<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>

~~META:title =November 17, 2016~~
<WRAP 70% center>
^  **DATE:**|Thursday, November 17, 2016 |
^  **TIME:**|1:15pm to 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Ruiqi Liu, Binghamton University |
^  **TITLE:**|Statistical inference on panel data: a kernel ridge regression approach|
</WRAP>
\\ 

<WRAP center box 80%>
<WRAP centeralign>**Abstract**</WRAP>

Panel Data is common in financial and economical areas. Various models have been developed to explain the relationship between variables. For example, the classic random/fixed effect model can discover the individual effects and common effects among different subjects, while the common factor model is able to characterize the cross-sectional factors with different individual factor loading. In our work, a non-parametric interactive fixed effects (NIFE) model, unifying the existing popular models, is considered which includes a heterogeneous or homogeneous non-parametric component, an unobservable cross-sectional factor and unobservable factor loading. We propose a kernel ridge regression approach to estimate the non-parametric function and model parameters. Convergence rate and asymptotic normality are established in both heterogeneous and homogeneous cases. Numerical evidence supports our results.
</WRAP>