1 resultado para Interval forecasting
em Brock University, Canada
Filtro por publicador
- Repository Napier (1)
- Aberystwyth University Repository - Reino Unido (1)
- Academic Research Repository at Institute of Developing Economies (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (6)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (10)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Aquatic Commons (9)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (12)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (8)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (2)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (27)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CaltechTHESIS (2)
- Cambridge University Engineering Department Publications Database (8)
- CentAUR: Central Archive University of Reading - UK (151)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (14)
- Cochin University of Science & Technology (CUSAT), India (3)
- Collection Of Biostatistics Research Archive (8)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (6)
- CORA - Cork Open Research Archive - University College Cork - Ireland (3)
- CUNY Academic Works (3)
- Dalarna University College Electronic Archive (5)
- Department of Computer Science E-Repository - King's College London, Strand, London (2)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Commons @ DU | University of Denver Research (1)
- Digital Peer Publishing (1)
- DigitalCommons@The Texas Medical Center (6)
- DigitalCommons@University of Nebraska - Lincoln (2)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (6)
- Greenwich Academic Literature Archive - UK (1)
- Helda - Digital Repository of University of Helsinki (12)
- Indian Institute of Science - Bangalore - Índia (24)
- Instituto Politécnico do Porto, Portugal (8)
- Massachusetts Institute of Technology (2)
- Ministerio de Cultura, Spain (1)
- National Center for Biotechnology Information - NCBI (7)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (8)
- Publishing Network for Geoscientific & Environmental Data (212)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (43)
- Queensland University of Technology - ePrints Archive (45)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (4)
- Repositório digital da Fundação Getúlio Vargas - FGV (21)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (62)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (3)
- Universidad Politécnica de Madrid (19)
- Universidade Complutense de Madrid (2)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (1)
- Universitat de Girona, Spain (7)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (8)
- University of Connecticut - USA (2)
- University of Michigan (52)
- University of Queensland eSpace - Australia (6)
- WestminsterResearch - UK (3)
- Worcester Research and Publications - Worcester Research and Publications - UK (2)
Resumo:
For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields