TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t -Distribution
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| Format: | Artículo científico |
| Language: | en |
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Universidad Nacional de Colombia
2015
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| author | Hanwen Zhang |
| author_facet | Hanwen Zhang |
| contents | TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t -Distribution Hanwen Zhang Fabio H. Nieto Física, Astronomía y Matemáticas Forecasting Time Series Missing Data Gibbs Sampler Bayesian Statistics This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t -dis- tribution. The analysis is done in the presence of missing data in both the threshold process f Z t g and the interest process f X t g . 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. 2015 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89938627014 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.1 Vol.38 |
| format | Artículo científico |
| id | redalyc_89938627014 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t -Distribution Hanwen Zhang Física, Astronomía y Matemáticas Forecasting Time Series Missing Data Gibbs Sampler Bayesian Statistics TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t -Distribution Hanwen Zhang Fabio H. Nieto Física, Astronomía y Matemáticas Forecasting Time Series Missing Data Gibbs Sampler Bayesian Statistics This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t -dis- tribution. The analysis is done in the presence of missing data in both the threshold process f Z t g and the interest process f X t g . 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. 2015 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89938627014 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.1 Vol.38 |
| title | TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t -Distribution |
| topic | Física, Astronomía y Matemáticas Forecasting Time Series Missing Data Gibbs Sampler Bayesian Statistics |
| url | https://www.redalyc.org/articulo.oa?id=89938627014 |