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), 2023. Published by Science Publishing Group

Keywords

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

References

[1]	Billio, M., Caporin, M. & Cazzavillan, G. (2008). Dating EU15 Monthly Business Cycle Jointly Using GDP and IPI. Journal of Business Cycle Measurement and Analysis 2007/3: 333-366. DOI: https://doi.org/10.1787/jbcma-v2007-art16-en.
[2]	Boot, J. C. G., Feibes, W. &Lisman, J. H. C. (1967). Further methods of derivation of quarterly figures from annual data. Applied Statistics 16: 65-75. DOI: https://doi.org/10.2307/2985238.
[3]	Bournay, J. & Laroque, G. (1979). Réflexions sur la méthode d´élaboration des comptes trimestriels. Annales de l´INSEE 36: 3-30. DOI: https://doi.org/10.2307/20075332.
[4]	Brunhart, A. (2012). Identification of Liechtenstein's historic economic growth and business cycles by econometric extensions of data series. KOFL Working Papers 14. Universität Liechtenstein, Konjunkturforschungsstelle (KOFL), Vaduz. Available at: http://hdl.handle.net/10419/97330.
[5]	Caporin, M. & Sartore, D. (2006). Methodological Aspects of Time Series Back-Calculation. University Ca' Foscari of Venice, Dept. of Economics. Research Paper Series 56/06. DOI: http://dx.doi.org/10.2139/ssrn.950923.
[6]	Chow, G. & Lin, A. L. (1971). Best linear unbiased interpolation, distribution and extrapolation of time series by related series. Review of Economics and Statistics 43: 372 375. DOI: https://doi.org/10.2307/1928739.
[7]	Denton, F. T. (1971). Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization. Journal of the American Statistical Association 66: 99-102. DOI: https://doi.org/10.2307/2284856.
[8]	Di Fonzo, T. (1987). La stima indiretta di serie economiche trimestrali. Padova: Cleup.
[9]	Dureau, G. (1991). Les comptes nationaux trimestriels. INSEE – Méthodes 13. Paris: INSEE.
[10]	Eurostat. (2013). Handbook on quarterly national accounts. Luxembourg: Eurostat.
[11]	Fernández, R. B. (1981). A Methodological Note on the Estimation of Time Series. The Review of Economics and Statistics 63: 471–476. DOI: https://doi.org/10.2307/1924371.
[12]	Friedman, M. (1962). The interpolation of time series by related series. Journal of the American Statistical Association 57: 729 757. DOI: https://doi.org/10.2307/2281805.
[13]	Gregoir, S. (2003). Propositions pour une désagrégation temporelle basée sur des modeles dynamiques simples. In Workshop on Quarterly National Accounts. Paris-Bercy 05/12/1994-06/12/1994: 151-166. Luxembourg: Office for Official Publications of the European Communities. Available at: https://ec.europa.eu/eurostat/web/products-statistical-working-papers/-/ks-ao-02-001.
[14]	Guérois, M. et al. (2019). A harmonized database to follow the demographic trajectories of European cities, the TRADEVE database. Cybergeo: European Journal of Geography. Document 892. DOI: https://doi.org/10.4000/cybergeo.32077.
[15]	Guerrero, V. M. & Corona, F. (2018). Retropolating some relevant series of Mexico's System of National Accounts at constant prices: The case of Mexico City's GDP. Statistica Neerlandica 72: 495-519. DOI: https://doi.org/10.1111/stan.12162.
[16]	Kozák, J., Hindls, R. & Hronová, S. (2000). Some remarks to the methodology of the allocation of yearly observations into seasons. Statistics in Transition 4: 815–826.
[17]	Litterman, R. B. (1983). A Random Walk, Markov Model for the Distribution of Time Series. Journal of Business & Economic Statistics 1: 169–173. DOI: https://doi.org/10.2307/1391858.
[18]	Nasse, P. (1973). Le systeme des comptes nationaux trimestriels. Annales de l´INSEE 14: 119-165. DOI: https://doi.org/10.2307/20075174.
[19]	Pfeuffer, V. & Scherb, B. (2016). Identification of New Business Fields and Development Directions Using Contradictions. Procedia CIRP 39: 197-202. DOI: https://doi.org/10.1016/j.procir.2016.01.188.
[20]	Prados de la Escosura, L. (2016). Mismeasuring Long Run Growth. The Bias from Spliced National Accounts. Cliometrica 10: 251-275. DOI: https://doi.org/10.1007/s11698-015-0131-4.
[21]	Prados de la Escosura, L. (2022). Capital in Spain 1850–2019. Cliometrica 16: 1–28. DOI: 10.1007/s11698-020-00221-2.
[22]	Reymann, F. (2013). Verfahren zur Strategieentwicklung und-umsetzung auf Basis einer Retropolation von Zukunftsszenarien. Paderborn: Heinz Nixdorf Institut.

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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