Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
In some fields, we are forced to work with missing data in multivariate time series, unfortunately the analysis in this context cannot be done as in the case of complete data. Bayesian analysis of multivariate thresholds autoregressive models(MTAR) with exogenous inputs and missing data is carried o...
- Autores:
-
Calderón Villanueva, Sergio Alejandro
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2014
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/52159
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/52159
http://bdigital.unal.edu.co/46427/
- Palabra clave:
- 51 Matemáticas / Mathematics
Bayesian Analysis
Bayesian variable selection
Monte Carlo Markov Chain
Missing data
Multivariate threshold autoregressive model
Análisis Bayesiano
Selección Bayesiana de variables
Cadenas de Markov Monte Carlo
Datos Faltantes
Modelos multivariados autoregressivos de umbrales
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | In some fields, we are forced to work with missing data in multivariate time series, unfortunately the analysis in this context cannot be done as in the case of complete data. Bayesian analysis of multivariate thresholds autoregressive models(MTAR) with exogenous inputs and missing data is carried out. MCMC methods are used to obtain samples from the marginal posterior distributions, including threshold values and missing data. In order to identify autoregressive orders, we adapt the Bayesian variable selection method to the MTAR models. The number of regimes is estimated using marginal likelihood and product space strategies. The forecasting of the output vector is implemented finding its predictive distributions. Simulation experiments and real data examples are presented. |
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