Flexible Latent Variable Methods for Panel Data

dc.contributor.advisor Kohn, Robert
dc.contributor.advisor Sisson, Scott
dc.contributor.advisor Fiebig, Denzil Balnozan, Igor 2022-08-24T00:44:59Z 2022-08-24T00:44:59Z 2022 2022-08-23T17:21:39Z
dc.description.abstract This thesis explores the development and novel application of linear panel data methods that use latent grouping variables in the modelling of time-varying unobservable heterogeneity. The methods are tailored for use in microeconomic applications with observational panel data by: a) controlling for individual-specific intercepts; b) focusing on the economic interpretability of the time-varying heterogeneity component of the models; c) addressing the problem of estimating unknown group memberships across an unknown number of latent groups. The most general model studied also allows for latent group structures in the partial effects of observed covariates, where groups in the covariate effects can be independent from groups in the unobservable heterogeneity. Classical and Bayesian statistical methodologies are considered, with the main methodological contributions being in the development of Bayesian approaches. For the kinds of applications studied, the Bayesian methods are shown to have more favourable properties, both in principle and in practice. Empirical applications to retirement decumulation and smoking policy in Australia demonstrate how the methods developed in this thesis may be used to learn about economically meaningful latent behavioural patterns across a range of applications.
dc.language English
dc.language.iso en
dc.publisher UNSW, Sydney
dc.rights CC BY 4.0
dc.subject.other Panel data econometrics
dc.subject.other Bayesian econometrics
dc.subject.other Retirement incomes
dc.title Flexible Latent Variable Methods for Panel Data
dc.type Thesis
dcterms.accessRights open access
dcterms.rightsHolder Balnozan, Igor
dspace.entity.type Publication
unsw.accessRights.uri 2022-08-23
unsw.relation.faculty Business
unsw.relation.faculty Science School of Mathematics & Statistics School of Economics School of Economics
unsw.subject.fieldofresearchcode 380204 Panel data analysis
unsw.thesis.degreetype PhD Doctorate
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