##Statistics Seminar##\\ Department of Mathematical Sciences
^ **DATE:**|Thursday, Month 31, 2017 |
^ **TIME:**|1:15pm -- 2:15pm |
^ **LOCATION:**|WH 100E |
^ **SPEAKER:**|Ruiqi Liu, Binghamton University |
^ **TITLE:**| Identification and estimation in panel models with overspecified number of groups |
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**Abstract**
In this paper, we provide a simple approach to identify and estimate group structure in panel models by adapting the M-estimation method. We consider both
linear and nonlinear panel models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership
is unknown to researchers. The main result of the paper is that under certain assumptions, our approach is able to provide uniformly consistent group parameter
estimator as long as the number of groups used in estimation is not smaller than
the true number of groups. We also show that, with probability approaching one,
our method can partition some true groups into further subgroups, but cannot
mix individuals from different groups. When the true number of groups is used
in estimation, all the individuals can be categorized correctly with probability
approaching one, and we establish the limiting distribution for the estimates of
the group parameters. In addition, we provide an information criterion to choose
the number of group and established its consistency under some mild conditions.
Monte Carlo simulations are conducted to examine the finite sample performance
of our proposed method. Findings in the simulation confirm our theoretical results
in the paper. Application to labor force participation also highlights the necessity
to take into account of individual heterogeneity and group heterogeneity.