Wednesday 27 February 2019, 1.00PM to 2.15pm
Speaker(s): Wendun Wang (Erasmus)
Abstract: Panel data are often characterized by cross-sectional heterogeneity, and a flexible yet parsimonious way of modelling heterogeneity is to cluster units into groups. A group pattern of heterogeneity may exist not only in the mean but also in the other characteristics of the distribution. To identify latent groups and recover the heterogeneous distribution, we propose a clustering method based on composite quantile regressions. We show that combining the strength across multiple panel quantile regression models improves the precision of the group membership estimates if the group structure is common across quantiles. Asymptotic theories for the proposed estimators are established, while their finite-sample performance is demonstrated by simulations. We finally apply the proposed methods to analyse the cross-country output effect of infrastructure capital.
Location: Alan Maynard Auditorium (R/C/014)
Admission: All welcome