主  題:Quantile-regression-based clustering for panel data

內(nèi)容簡(jiǎn)介:In many applications it is important to identify subgroups of units with heterogeneous parameters. We propose a new quantile-regression-based method for panel data to identify subgroups and estimate group-specific parameters. In practice the signal differentiating subgroups may vary across quantiles though the group membership may be common. It remains unclear which quantile is preferable or should one combine information across quantiles to perform clustering. To answer this question, we consider a stability measure to choose among single quantiles and the composite quantile. We establish the asymptotic properties of the proposed estimators, and assess their performance through simulation and the analysis of an economic growth data.

報(bào)告人:朱仲義      教授    博導(dǎo)

時(shí)  間:2019-03-09    09:00

地  點(diǎn):競(jìng)慧東樓302

舉辦單位:統(tǒng)計(jì)與數(shù)學(xué)學(xué)院  統(tǒng)計(jì)科學(xué)與大數(shù)據(jù)研究院