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dc.identifier.urihttp://hdl.handle.net/11401/77443
dc.description.sponsorshipThis work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.en_US
dc.formatMonograph
dc.format.mediumElectronic Resourceen_US
dc.language.isoen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.
dc.typeDissertation
dcterms.abstractFactor-based models have been extensively used in economic and financial time series analyses. The Factor-augmented Error Correction Model (FECM) is a successful generalization of the Factor-augmented Vector Autoregression Model and the Error Correction Model for large panel nonstationary time series data. By combining the factors and error correction terms together, the FECM is able to utilize both the aggregated panel information summarized through the Dynamic Factor Model as well as the long-term equilibrium information introduced by the cointegration relationship. In this thesis we extend the FECM by allowing time-varying model parameters. There are ample evidences from both theoretical and empirical studies supporting the notion that the parameters of economic and financial models often change over time. By relaxing the parameters to be time-varying, the model will be more adaptable to complicated and realistic data structures, such as those with potential structural instability after a recession or crisis. We conclude this thesis by applying the newly developed time-varying FECM to provide more suitable models for PPNR (Pre-Provision Net Revenue) studies, part of the required modeling process in CCAR (Comprehensive Capital Analysis and Review) -- commonly known as the Federal Reserve’s Stress Test on big banks and other financial institutes.
dcterms.abstractFactor-based models have been extensively used in economic and financial time series analyses. The Factor-augmented Error Correction Model (FECM) is a successful generalization of the Factor-augmented Vector Autoregression Model and the Error Correction Model for large panel nonstationary time series data. By combining the factors and error correction terms together, the FECM is able to utilize both the aggregated panel information summarized through the Dynamic Factor Model as well as the long-term equilibrium information introduced by the cointegration relationship. In this thesis we extend the FECM by allowing time-varying model parameters. There are ample evidences from both theoretical and empirical studies supporting the notion that the parameters of economic and financial models often change over time. By relaxing the parameters to be time-varying, the model will be more adaptable to complicated and realistic data structures, such as those with potential structural instability after a recession or crisis. We conclude this thesis by applying the newly developed time-varying FECM to provide more suitable models for PPNR (Pre-Provision Net Revenue) studies, part of the required modeling process in CCAR (Comprehensive Capital Analysis and Review) -- commonly known as the Federal Reserve’s Stress Test on big banks and other financial institutes.
dcterms.available2017-09-20T16:52:42Z
dcterms.contributorZhu, Weien_US
dcterms.contributorWang, Xuefengen_US
dcterms.contributorWu, Songen_US
dcterms.contributorXiao, Keli.en_US
dcterms.creatorHao, Xue
dcterms.dateAccepted2017-09-20T16:52:42Z
dcterms.dateSubmitted2017-09-20T16:52:42Z
dcterms.descriptionDepartment of Applied Mathematics and Statistics.en_US
dcterms.extent89 pg.en_US
dcterms.formatMonograph
dcterms.formatApplication/PDFen_US
dcterms.identifierhttp://hdl.handle.net/11401/77443
dcterms.issued2015-05-01
dcterms.languageen_US
dcterms.provenanceMade available in DSpace on 2017-09-20T16:52:42Z (GMT). No. of bitstreams: 1 Hao_grad.sunysb_0771E_12552.pdf: 6032684 bytes, checksum: ffd228e2bbffae8471fbf3507f043743 (MD5) Previous issue date: 2015en
dcterms.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.
dcterms.subjectStatistics
dcterms.titleFactor-Augmented Error Correction Model with Time Varying Coefficients
dcterms.typeDissertation


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