{"title":"Heterogeneous Grouping Structures in Panel Data","authors":"Katerina Chrysikou, George Kapetanios","doi":"arxiv-2407.19509","DOIUrl":null,"url":null,"abstract":"In this paper we examine the existence of heterogeneity within a group, in\npanels with latent grouping structure. The assumption of within group\nhomogeneity is prevalent in this literature, implying that the formation of\ngroups alleviates cross-sectional heterogeneity, regardless of the prior\nknowledge of groups. While the latter hypothesis makes inference powerful, it\ncan be often restrictive. We allow for models with richer heterogeneity that\ncan be found both in the cross-section and within a group, without imposing the\nsimple assumption that all groups must be heterogeneous. We further contribute\nto the method proposed by \\cite{su2016identifying}, by showing that the model\nparameters can be consistently estimated and the groups, while unknown, can be\nidentifiable in the presence of different types of heterogeneity. Within the\nsame framework we consider the validity of assuming both cross-sectional and\nwithin group homogeneity, using testing procedures. Simulations demonstrate\ngood finite-sample performance of the approach in both classification and\nestimation, while empirical applications across several datasets provide\nevidence of multiple clusters, as well as reject the hypothesis of within group\nhomogeneity.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.19509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper we examine the existence of heterogeneity within a group, in
panels with latent grouping structure. The assumption of within group
homogeneity is prevalent in this literature, implying that the formation of
groups alleviates cross-sectional heterogeneity, regardless of the prior
knowledge of groups. While the latter hypothesis makes inference powerful, it
can be often restrictive. We allow for models with richer heterogeneity that
can be found both in the cross-section and within a group, without imposing the
simple assumption that all groups must be heterogeneous. We further contribute
to the method proposed by \cite{su2016identifying}, by showing that the model
parameters can be consistently estimated and the groups, while unknown, can be
identifiable in the presence of different types of heterogeneity. Within the
same framework we consider the validity of assuming both cross-sectional and
within group homogeneity, using testing procedures. Simulations demonstrate
good finite-sample performance of the approach in both classification and
estimation, while empirical applications across several datasets provide
evidence of multiple clusters, as well as reject the hypothesis of within group
homogeneity.