Generalized Principal Component Analysis

Simple item page
dc.contributor.authorSolat, Karoen
dc.contributor.committeechairSpanos, Arisen
dc.contributor.committeememberBahel, Eric A.en
dc.contributor.committeememberAshley, Richard A.en
dc.contributor.committeememberTsang, Kwok Pingen
dc.contributor.committeememberMaasoumi, Esfandiaren
dc.contributor.departmentEconomics, Scienceen
dc.date.accessioned2018-06-06T08:02:09Zen
dc.date.available2018-06-06T08:02:09Zen
dc.date.issued2018-06-05en
dc.description.abstractThe primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA). The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:15413en
dc.identifier.urihttp://hdl.handle.net/10919/83469en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectFactor Modelen
dc.subjectPCAen
dc.subjectElliptically Contoured Distributionsen
dc.subjectExchange Rateen
dc.subjectForecastingen
dc.subjectMonte Carlo Simulationen
dc.subjectStatistical Adequacyen
dc.titleGeneralized Principal Component Analysisen
dc.typeDissertationen
thesis.degree.disciplineEconomicsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Solat_K_D_2018.pdf
Size:
2.47 MB
Format:
Adobe Portable Document Format
Download

Collections

Doctoral Dissertations