The functional time series (FTS) models are used for analyzing, modeling and forecasting age-specific mortality rates. However, the application of these models in presence of two or more groups within similar populations needs some modification. In these cases, it is desirable for the disaggregated forecasts to be coherent with the overall forecast. The 'coherent' forecasts are the non-divergent forecasts of sub-groups within a population. Reference [1] first proposed a coherent functional model based on product and ratios of mortality rates. In this paper, we relate some of the functional time series models to the common principal components (CPC) and partial common principal components (PCPC) models introduced by [2] and provide the methods to estimate these models. We call them common functional principal component (CFPC) models and use them for coherent mortality forecasting. Here, we propose a sequential procedure based on Johansen methodology to estimate the model parameters. We use vector approach and make use of error correction models to forecast the specific time series coefficient for each sub-group.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 1) |
| DOI | 10.11648/j.sjams.20150301.14 |
| Page(s) | 22-26 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Mortality, Forecast, Coherent Forecasts, Functional Data, Life Expectancy, Sex-Ratio, Cointegration
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APA Style
Farah Yasmeen. (2015). The Common Principal Component (CPC) Approach to Functional time Series (FTS) Models. Science Journal of Applied Mathematics and Statistics, 3(1), 22-26. https://doi.org/10.11648/j.sjams.20150301.14
ACS Style
Farah Yasmeen. The Common Principal Component (CPC) Approach to Functional time Series (FTS) Models. Sci. J. Appl. Math. Stat. 2015, 3(1), 22-26. doi: 10.11648/j.sjams.20150301.14
AMA Style
Farah Yasmeen. The Common Principal Component (CPC) Approach to Functional time Series (FTS) Models. Sci J Appl Math Stat. 2015;3(1):22-26. doi: 10.11648/j.sjams.20150301.14
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Download@article{10.11648/j.sjams.20150301.14,
author = {Farah Yasmeen},
title = {The Common Principal Component (CPC) Approach to Functional time Series (FTS) Models},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {3},
number = {1},
pages = {22-26},
doi = {10.11648/j.sjams.20150301.14},
url = {https://doi.org/10.11648/j.sjams.20150301.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150301.14},
abstract = {The functional time series (FTS) models are used for analyzing, modeling and forecasting age-specific mortality rates. However, the application of these models in presence of two or more groups within similar populations needs some modification. In these cases, it is desirable for the disaggregated forecasts to be coherent with the overall forecast. The 'coherent' forecasts are the non-divergent forecasts of sub-groups within a population. Reference [1] first proposed a coherent functional model based on product and ratios of mortality rates. In this paper, we relate some of the functional time series models to the common principal components (CPC) and partial common principal components (PCPC) models introduced by [2] and provide the methods to estimate these models. We call them common functional principal component (CFPC) models and use them for coherent mortality forecasting. Here, we propose a sequential procedure based on Johansen methodology to estimate the model parameters. We use vector approach and make use of error correction models to forecast the specific time series coefficient for each sub-group.},
year = {2015}
}
TY - JOUR T1 - The Common Principal Component (CPC) Approach to Functional time Series (FTS) Models AU - Farah Yasmeen Y1 - 2015/02/09 PY - 2015 N1 - https://doi.org/10.11648/j.sjams.20150301.14 DO - 10.11648/j.sjams.20150301.14 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 22 EP - 26 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20150301.14 AB - The functional time series (FTS) models are used for analyzing, modeling and forecasting age-specific mortality rates. However, the application of these models in presence of two or more groups within similar populations needs some modification. In these cases, it is desirable for the disaggregated forecasts to be coherent with the overall forecast. The 'coherent' forecasts are the non-divergent forecasts of sub-groups within a population. Reference [1] first proposed a coherent functional model based on product and ratios of mortality rates. In this paper, we relate some of the functional time series models to the common principal components (CPC) and partial common principal components (PCPC) models introduced by [2] and provide the methods to estimate these models. We call them common functional principal component (CFPC) models and use them for coherent mortality forecasting. Here, we propose a sequential procedure based on Johansen methodology to estimate the model parameters. We use vector approach and make use of error correction models to forecast the specific time series coefficient for each sub-group. VL - 3 IS - 1 ER -
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