IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/53389.html
   My bibliography  Save this paper

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. 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.
    2. 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.
    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. Massimo Gerli & Giovanni Marini, 2006. "Spatial and Temporal Time Series Conversion: A Consistent Estimator of the Error Variance-Covariance Matrix," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 373-405.
    2. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    3. 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.
    4. 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.
    5. Richard M. Todd, 1988. "Implementing Bayesian vector autoregressions," Working Papers 384, Federal Reserve Bank of Minneapolis.
    6. 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.
    7. 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.
    8. Cuevas Ángel & Quilis Enrique M. & Espasa Antoni, 2015. "Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking," Journal of Official Statistics, Sciendo, vol. 31(4), pages 627-647, December.
    9. Tommaso Proietti, 2004. "On the Estimation of Nonlinearly Aggregated Mixed Models," Econometrics 0411012, University Library of Munich, Germany.
    10. Tommaso Proietti, 2011. "Multivariate temporal disaggregation with cross-sectional constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1455-1466, June.
    11. Bernardí Cabred & Jose Pavía, 1999. "EstimatingJ (>1) quarterly time series in fulfilling annual and quarterly constraints," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(3), pages 339-349, August.
    12. Jürgen Bierbaumer-Polly & Sandra Bilek-Steindl, 2017. "Quarterly National Accounts – Manual for Austria. Description of Applied Methods and Data Sources," WIFO Studies, WIFO, number 60427, April.
    13. Wolfgang Polasek & Richard Sellner, 2008. "Spatial Chow-Lin Methods: Bayesian And Ml Forecast Comparisons," Working Paper series 38_08, Rimini Centre for Economic Analysis.
    14. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    15. 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.
    16. Raffaella Basile & Bruno Chiarini & Elisabetta Marzano, 2011. "Can we Rely upon Fiscal Policy Estimates in Countries with Unreported Production of 15 Per Cent (or more) of GDP?," CESifo Working Paper Series 3521, CESifo.
    17. 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.
    18. Kahouli, Sondès, 2011. "Re-examining uranium supply and demand: New insights," Energy Policy, Elsevier, vol. 39(1), pages 358-376, January.
    19. Cecilia Frale, "undated". "Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?," Working Papers wp2008-2, Department of the Treasury, Ministry of the Economy and of Finance.
    20. 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.

    More about this item

    Keywords

    temporal disaggregation; time series; Chow-Lin; Denton; Fernandez; Litterman;
    All these 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.