1 resultado para Hydrodynamic weather forecasting.
em Brock University, Canada
Filtro por publicador
- Repository Napier (2)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- Adam Mickiewicz University Repository (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- Applied Math and Science Education Repository - Washington - USA (1)
- Aquatic Commons (18)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (4)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (4)
- Aston University Research Archive (7)
- Avian Conservation and Ecology - Eletronic Cientific Hournal - Écologie et conservation des oiseaux: (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (3)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (2)
- Brock University, Canada (1)
- CaltechTHESIS (3)
- Cambridge University Engineering Department Publications Database (35)
- CentAUR: Central Archive University of Reading - UK (338)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (25)
- Cochin University of Science & Technology (CUSAT), India (10)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (1)
- CORA - Cork Open Research Archive - University College Cork - Ireland (4)
- CUNY Academic Works (7)
- Dalarna University College Electronic Archive (7)
- Digital Archives@Colby (2)
- DigitalCommons@The Texas Medical Center (1)
- Duke University (3)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (10)
- Greenwich Academic Literature Archive - UK (2)
- Helda - Digital Repository of University of Helsinki (23)
- Indian Institute of Science - Bangalore - Índia (45)
- Instituto Politécnico do Porto, Portugal (6)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (1)
- Massachusetts Institute of Technology (1)
- Ministerio de Cultura, Spain (11)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (23)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (55)
- Queensland University of Technology - ePrints Archive (76)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (4)
- Repositório digital da Fundação Getúlio Vargas - FGV (21)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (43)
- 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 (7)
- School of Medicine, Washington University, United States (8)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (1)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (1)
- Universitat de Girona, Spain (2)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (5)
- University of Connecticut - USA (1)
- University of Michigan (33)
- University of Queensland eSpace - Australia (5)
- University of Washington (1)
- WestminsterResearch - UK (5)
- Worcester Research and Publications - Worcester Research and Publications - UK (8)
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