{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124632"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124632","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Estimation and forecasting with time-varying parameters models and sequential method","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Sun, Zhendong"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Amir-Ahmadi, Pooyan","Bernhardt, Dan","Xie, Shihan","Chen, Yuguo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Economic Forecasting","Time-varying Parameters Model","Sequential Monte Carlo"],"languages":["en","eng"],"rights":["Copyright 2024 Zhendong Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124632","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Amir-Ahmadi, Pooyan","Bernhardt, Dan","Xie, Shihan","Chen, Yuguo"]},{"key":"dc:creator","label":"Author","values":["Sun, Zhendong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-03-29"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economic Forecasting","Time-varying Parameters Model","Sequential Monte Carlo"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Zhendong Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124632"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Zhendong Sun, accepted the attached license on 2024-03-19 at 09:03.","The student, Zhendong Sun, submitted this Dissertation for approval on 2024-03-19 at 09:10.","This Dissertation was approved for publication on 2024-03-29 at 16:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20257 on 2024-09-16 at 00:48:52","In this research, we examine the use of time-varying parameters (TVP) models for out-of-sample forecasting within the realms of macroeconomics and finance. From a methodological perspective, the efficacy of the Sequential Monte Carlo (SMC) method in estimating TVP models is emphasized. Notably, SMC provides a distinct computational edge, requiring substantially less processing time relative to the traditional Markov Chain Monte Carlo (MCMC) method, all the while preserving predictive accuracy. Furthermore, we augment a generic SMC approach by incorporating the variational Bayes method, thereby enabling it to estimate large TVP models with an integrated variable selection prior. Empirically, we embark on a detailed exploration of three out-of-sample predictive applications in the fields of macroeconomics and finance: 1) the estimation of US GDP and inflation via a trivariate VAR model; 2) the forecasting of monthly returns of the S$\\&$P500 index, which integrates a comprehensive set of 143 predictors; and 3) the nowcasting of US GDP using a TVP VAR model enriched with mixed-frequency variables. Consistently, across these analytical domains, findings suggest that TVP models bolster predictive capabilities, surpassing both their fixed-parameter counterparts and other advanced methodologies."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Estimation and forecasting with time-varying parameters models and sequential method"]}]}],"canonical_facts":{"dc:contributor":["Amir-Ahmadi, Pooyan","Bernhardt, Dan","Xie, Shihan","Chen, Yuguo"],"dc:creator":["Sun, Zhendong"],"dc:date":["2024-05","2024-03-29"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Zhendong Sun, accepted the attached license on 2024-03-19 at 09:03.","The student, Zhendong Sun, submitted this Dissertation for approval on 2024-03-19 at 09:10.","This Dissertation was approved for publication on 2024-03-29 at 16:14.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20257 on 2024-09-16 at 00:48:52","In this research, we examine the use of time-varying parameters (TVP) models for out-of-sample forecasting within the realms of macroeconomics and finance. From a methodological perspective, the efficacy of the Sequential Monte Carlo (SMC) method in estimating TVP models is emphasized. Notably, SMC provides a distinct computational edge, requiring substantially less processing time relative to the traditional Markov Chain Monte Carlo (MCMC) method, all the while preserving predictive accuracy. Furthermore, we augment a generic SMC approach by incorporating the variational Bayes method, thereby enabling it to estimate large TVP models with an integrated variable selection prior. Empirically, we embark on a detailed exploration of three out-of-sample predictive applications in the fields of macroeconomics and finance: 1) the estimation of US GDP and inflation via a trivariate VAR model; 2) the forecasting of monthly returns of the S$\\&$P500 index, which integrates a comprehensive set of 143 predictors; and 3) the nowcasting of US GDP using a TVP VAR model enriched with mixed-frequency variables. Consistently, across these analytical domains, findings suggest that TVP models bolster predictive capabilities, surpassing both their fixed-parameter counterparts and other advanced methodologies."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124632"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Zhendong Sun"],"dc:subject":["Economic Forecasting","Time-varying Parameters Model","Sequential Monte Carlo"],"dc:title":["Estimation and forecasting with time-varying parameters models and sequential method"],"dc:type":["text"],"thesis:degree_discipline":["Economics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}