Submitted by philip semple (philip.semple@gmail.com) on 2013-03-05T15:18:19Z
No. of bitstreams: 1
Dissert_v2.pdf: 1128003 bytes, checksum: 0763467efee5097f1860a76603845b42 (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-03-05T17:17:49Z (GMT) No. of bitstreams: 1
Dissert_v2.pdf: 1128003 bytes, checksum: 0763467efee5097f1860a76603845b42 (MD5) / Made available in DSpace on 2013-03-05T17:32:21Z (GMT). No. of bitstreams: 1
Dissert_v2.pdf: 1128003 bytes, checksum: 0763467efee5097f1860a76603845b42 (MD5)
Previous issue date: 2013-02-04 / The purpose of this study is to develop econometric models for time series prediction of the behavior of aggregate delinquency using a broad set of information through the FAVAR (Factor-Augmented Vector Autoregressive) of Bernanke, Boivin and Eliasz (2005) and FAVECM (Factor-augmented Error Correction Models) of Baneerjee and Marcellino (2008) methods. From this, out of sample forecasts were made in order to compare the effectiveness of predicting models against simple univariate models; ARIMA (model autoregressive integrated moving average) and SARIMA (seasonal autoregressive integrated moving average). To evaluate the predictive efficiency of the methodologies was used Hansen, Lunde e James (2011) MCS (Model Confidence Set) method. This methodology allows comparing the superiority of one or more forecasting models against other models. / O objetivo do presente trabalho é utilizar modelos econométricos de séries de tempo para previsão do comportamento da inadimplência agregada utilizando um conjunto amplo de informação, através dos métodos FAVAR (Factor-Augmented Vector Autoregressive) de Bernanke, Boivin e Eliasz (2005) e FAVECM (Factor-augmented Error Correction Models) de Baneerjee e Marcellino (2008). A partir disso, foram construídas previsões fora da amostra de modo a comparar a eficácia de projeção dos modelos contra modelos univariados mais simples - ARIMA - modelo auto-regressivo integrado de média móvel e SARIMA - modelo sazonal auto-regressivo integrado de média móvel. Para avaliação da eficácia preditiva foi utilizada a metodologia MCS (Model Confidence Set) de Hansen, Lunde e James (2011) Essa metodologia permite comparar a superioridade de modelos temporais vis-à-vis a outros modelos.
| Identifer | oai:union.ndltd.org:IBICT/oai:bibliotecadigital.fgv.br:10438/10584 |
| Date | 04 February 2013 |
| Creators | Semple, Philip Alexander |
| Contributors | Pereira, Pedro L. Valls, Nishijima, Marislei, Escolas::EESP, Marçal, Emerson Fernandes |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional do FGV, instname:Fundação Getulio Vargas, instacron:FGV |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0029 seconds