IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/53389.html

Temporal Disaggregation of Time Series

Author

Listed:
  • Sax, Christoph
  • Steiner, Peter

Abstract

Temporal disaggregation methods are used to disaggregate low frequency time series to higher frequency series, where either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. The package tempdisagg is a collection of several methods for temporal disaggregation.

Suggested Citation

  • Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:53389
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/53389/1/sax-steiner.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    2. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    3. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-476, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim Abildgren, 2016. "A century of macro-financial linkages," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 8(4), pages 458-471, November.
    2. Pieroni, Luca & d'Agostino, Giorgio & Lorusso, Marco, 2008. "Can we declare military Keynesianism dead?," Journal of Policy Modeling, Elsevier, vol. 30(5), pages 675-691.
    3. Jürgen Bierbaumer & Sandra Bilek-Steindl, 2017. "Quarterly National Accounts – Manual for Austria. Description of Applied Methods and Data Sources," WIFO Studies, WIFO, number 60427.
    4. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    5. David Aristei & Luca Pieroni, 2005. "Estimating the Role of Government Expenditure in Long-run Consumption," Quaderni del Dipartimento di Economia, Finanza e Statistica 13/2005, Università di Perugia, Dipartimento Economia.
    6. Simeon Vosen & Torsten Schmidt, 2012. "A monthly consumption indicator for Germany based on Internet search query data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(7), pages 683-687, May.
    7. Thomas Raffinot, 2007. "A monthly indicator of GDP for Euro-Area based on business surveys," Applied Economics Letters, Taylor & Francis Journals, vol. 14(4), pages 267-270.
    8. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    9. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    10. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.
    11. Cuevas Rumín, Ángel & Quilis, Enrique M. & Espasa, Antoni, 2011. "Combining benchmarking and chain-linking for short-term regional forecasting," DES - Working Papers. Statistics and Econometrics. WS ws114130, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Marcus Scheiblecker & Sandra Bilek-Steindl & Michael Wüger, 2007. "Quarterly National Accounts Inventory of Austria. Description of Applied Methods and Data Sources," WIFO Studies, WIFO, number 37249.
    13. Hall, Viv & John, McDermott, "undated". "A Quarterly Post-World War II Real GDP Series for New Zealand," Motu Working Papers 292854, Motu Economic and Public Policy Research.
    14. Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April.
    15. Nijman, Theo E & Palm, Franz C, 1990. "Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 405-415, October.
    16. Willie Lahari & Alfred A. Haug & Arlene Garces-Ozanne, 2011. "Estimating Quarterly Gdp Data For The South Pacific Island Nations," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 56(01), pages 97-112.
    17. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    18. Alejandro Rodríguez Caro & Santiago Rodríguez Feijoo & Delia Dávila Quintana, 2003. "La trimestralización de variables flujo. Un estudio de simulación de los métodos de desagregación temporal con indicador," Documentos de trabajo conjunto ULL-ULPGC 2003-01, Facultad de Ciencias Económicas de la ULPGC.
    19. José Casals & Miguel Jerez & Sonia Sotoca, 2009. "Modelling and forecasting time series sampled at different frequencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 316-342.
    20. Vladim r Hajko, 2015. "Energy-Gross Domestic Product Nexus: Disaggregated Analysis for the Czech Republic in the Post-Transformation Era," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 869-888.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:53389. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.