TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution
This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t-distribution. The analysis is done in the presence of missing data in both the threshold process {Zt} and the interest process {Xt}. We develop a three-stage...
- Autores:
-
Zhang, Hanwen
Nieto, Fabio H.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/66551
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/66551
http://bdigital.unal.edu.co/67579/
- Palabra clave:
- 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Bayesian Statistics
Gibbs Sampler
Missing Data
Forecasting
Time Series
Threshold Autoregressive Model
Datos faltantes
Estadística Bayesiana
Modelo autoregresivo de umbrales
Muestreador de Gibbs
Pronóstico
Series de tiempo
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t-distribution. The analysis is done in the presence of missing data in both the threshold process {Zt} and the interest process {Xt}. We develop a three-stage procedure based on the Gibbs sampler in order to identify and estimate the model. Additionally, the estimation of the missing data and the forecasting procedure are provided. The proposed methodology is illustrated with simulated and real-life data. |
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