Method of Disaggregating Annual Time Series into Seasons

Richard Hindls; Stanislava Hronova

doi:doi:10.11648/j.ijsd.20230901.11

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Method of Disaggregating Annual Time Series into Seasons

Richard Hindls, Stanislava Hronova

Published in International Journal of Statistical Distributions and Applications (Volume 9, Issue 1)

Received: 15 January 2023 Accepted: 1 February 2023 Published: 9 February 2023

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Abstract

A number of indicators (especially economic ones), whose values are monitored with annual periodicity, need to be broken down into the so-called seasons, i.e., time periods shorter than one year. And it is necessary not only to respect the specifics of these seasons, but at the same time to do so without having to specifically survey these values in the seasons. The aim of the paper is to propose a method for distributing the annual values of an indicator into seasons (quarters, months) using a suitably chosen indicator, without subsequent correction. This will preserve the link to the processes evolving within the year, while removing the formalism of splitting the residual generated in the first step. The paper presents a new approach, in which simulated values of the response variables enable us to estimate the series' parameters with the aid of a simple loss function. Such a solution reduces the dependence of formal models on real data, which may or may not be available to the official statisticians who are responsible for statistical surveys. The paper shows the theoretical concept of the disaggregation method, which is the result of research in the given area and was verified on the example of data for the Czech Republic.

Published in	International Journal of Statistical Distributions and Applications (Volume 9, Issue 1)
DOI	10.11648/j.ijsd.20230901.11
Page(s)	1-8
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

Keywords

Disaggregation Methods, Retropolation, Seasonal Variations, National Accounts, Regression Model

References

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APA Style

Richard Hindls, Stanislava Hronova. (2023). Method of Disaggregating Annual Time Series into Seasons. International Journal of Statistical Distributions and Applications, 9(1), 1-8. https://doi.org/10.11648/j.ijsd.20230901.11

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ACS Style

Richard Hindls; Stanislava Hronova. Method of Disaggregating Annual Time Series into Seasons. Int. J. Stat. Distrib. Appl. 2023, 9(1), 1-8. doi: 10.11648/j.ijsd.20230901.11

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AMA Style

Richard Hindls, Stanislava Hronova. Method of Disaggregating Annual Time Series into Seasons. Int J Stat Distrib Appl. 2023;9(1):1-8. doi: 10.11648/j.ijsd.20230901.11

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@article{10.11648/j.ijsd.20230901.11,
  author = {Richard Hindls and Stanislava Hronova},
  title = {Method of Disaggregating Annual Time Series into Seasons},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {9},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.ijsd.20230901.11},
  url = {https://doi.org/10.11648/j.ijsd.20230901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20230901.11},
  abstract = {A number of indicators (especially economic ones), whose values are monitored with annual periodicity, need to be broken down into the so-called seasons, i.e., time periods shorter than one year. And it is necessary not only to respect the specifics of these seasons, but at the same time to do so without having to specifically survey these values in the seasons. The aim of the paper is to propose a method for distributing the annual values of an indicator into seasons (quarters, months) using a suitably chosen indicator, without subsequent correction. This will preserve the link to the processes evolving within the year, while removing the formalism of splitting the residual generated in the first step. The paper presents a new approach, in which simulated values of the response variables enable us to estimate the series' parameters with the aid of a simple loss function. Such a solution reduces the dependence of formal models on real data, which may or may not be available to the official statisticians who are responsible for statistical surveys. The paper shows the theoretical concept of the disaggregation method, which is the result of research in the given area and was verified on the example of data for the Czech Republic.},
 year = {2023}
}

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TY  - JOUR
T1  - Method of Disaggregating Annual Time Series into Seasons
AU  - Richard Hindls
AU  - Stanislava Hronova
Y1  - 2023/02/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ijsd.20230901.11
DO  - 10.11648/j.ijsd.20230901.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20230901.11
AB  - A number of indicators (especially economic ones), whose values are monitored with annual periodicity, need to be broken down into the so-called seasons, i.e., time periods shorter than one year. And it is necessary not only to respect the specifics of these seasons, but at the same time to do so without having to specifically survey these values in the seasons. The aim of the paper is to propose a method for distributing the annual values of an indicator into seasons (quarters, months) using a suitably chosen indicator, without subsequent correction. This will preserve the link to the processes evolving within the year, while removing the formalism of splitting the residual generated in the first step. The paper presents a new approach, in which simulated values of the response variables enable us to estimate the series' parameters with the aid of a simple loss function. Such a solution reduces the dependence of formal models on real data, which may or may not be available to the official statisticians who are responsible for statistical surveys. The paper shows the theoretical concept of the disaggregation method, which is the result of research in the given area and was verified on the example of data for the Czech Republic.
VL  - 9
IS  - 1
ER  -

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Author Information

Richard Hindls

Department of Statistics and Probability, Faculty of Informatics and Statistics, Prague University of Economics and Business, Prague, Czech Republic
Stanislava Hronova

Department of Economic Statistics, Faculty of Informatics and Statistics, Prague University of Economics and Business, Prague, Czech Republic

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APA Style

Richard Hindls, Stanislava Hronova. (2023). Method of Disaggregating Annual Time Series into Seasons. International Journal of Statistical Distributions and Applications, 9(1), 1-8. https://doi.org/10.11648/j.ijsd.20230901.11

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ACS Style

Richard Hindls; Stanislava Hronova. Method of Disaggregating Annual Time Series into Seasons. Int. J. Stat. Distrib. Appl. 2023, 9(1), 1-8. doi: 10.11648/j.ijsd.20230901.11

Copy | Download

AMA Style

Richard Hindls, Stanislava Hronova. Method of Disaggregating Annual Time Series into Seasons. Int J Stat Distrib Appl. 2023;9(1):1-8. doi: 10.11648/j.ijsd.20230901.11

Copy | Download

@article{10.11648/j.ijsd.20230901.11,
  author = {Richard Hindls and Stanislava Hronova},
  title = {Method of Disaggregating Annual Time Series into Seasons},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {9},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.ijsd.20230901.11},
  url = {https://doi.org/10.11648/j.ijsd.20230901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20230901.11},
  abstract = {A number of indicators (especially economic ones), whose values are monitored with annual periodicity, need to be broken down into the so-called seasons, i.e., time periods shorter than one year. And it is necessary not only to respect the specifics of these seasons, but at the same time to do so without having to specifically survey these values in the seasons. The aim of the paper is to propose a method for distributing the annual values of an indicator into seasons (quarters, months) using a suitably chosen indicator, without subsequent correction. This will preserve the link to the processes evolving within the year, while removing the formalism of splitting the residual generated in the first step. The paper presents a new approach, in which simulated values of the response variables enable us to estimate the series' parameters with the aid of a simple loss function. Such a solution reduces the dependence of formal models on real data, which may or may not be available to the official statisticians who are responsible for statistical surveys. The paper shows the theoretical concept of the disaggregation method, which is the result of research in the given area and was verified on the example of data for the Czech Republic.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Method of Disaggregating Annual Time Series into Seasons
AU  - Richard Hindls
AU  - Stanislava Hronova
Y1  - 2023/02/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ijsd.20230901.11
DO  - 10.11648/j.ijsd.20230901.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20230901.11
AB  - A number of indicators (especially economic ones), whose values are monitored with annual periodicity, need to be broken down into the so-called seasons, i.e., time periods shorter than one year. And it is necessary not only to respect the specifics of these seasons, but at the same time to do so without having to specifically survey these values in the seasons. The aim of the paper is to propose a method for distributing the annual values of an indicator into seasons (quarters, months) using a suitably chosen indicator, without subsequent correction. This will preserve the link to the processes evolving within the year, while removing the formalism of splitting the residual generated in the first step. The paper presents a new approach, in which simulated values of the response variables enable us to estimate the series' parameters with the aid of a simple loss function. Such a solution reduces the dependence of formal models on real data, which may or may not be available to the official statisticians who are responsible for statistical surveys. The paper shows the theoretical concept of the disaggregation method, which is the result of research in the given area and was verified on the example of data for the Czech Republic.
VL  - 9
IS  - 1
ER  -

Copy | Download