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Temporal disaggregation of economic time series: The view from the trenches


  • Enrique M. Quilis


We analyze temporal disaggregation from the point of view of practitioners working as producers of official statistics or as nowcasters. According to this view, applicability, computational feasibility, robustness, and ease of communication are key aspects of temporal disaggregation in addition to statistical soundness and flexibility. We review the models used as a workhorse of this approach, exploring in detail their similitudes and differences. All the models and techniques are examined through the lens of a complete set of MATLAB functions that have been developed for their use in a production model. A real‐time database comprising the Great Recession period is used to evaluate the alternative models and attribute the revisions to changes in the benchmark, seasonal adjustment, and extrapolations.

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  • Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
  • Handle: RePEc:bla:stanee:v:72:y:2018:i:4:p:447-470
    DOI: 10.1111/stan.12150

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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    3. Di Fonzo, Tommaso, 1990. "The Estimation of M Disaggregate Time Series When Contemporaneous and Temporal Aggregates Are Known," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 178-182, February.
    4. Baoline Chen, 2007. "An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts," BEA Papers 0077, Bureau of Economic Analysis.
    5. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    6. 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.
    7. 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.
    8. Milton Friedman, 1962. "Introduction to "The Interpolation of Time Series by Related Series"," NBER Chapters, in: The Interpolation of Time Series by Related Series, pages 1-3, National Bureau of Economic Research, Inc.
    9. Tommaso Di Fonzo & Marco Marini, 2011. "Simultaneous and two‐step reconciliation of systems of time series: methodological and practical issues," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(2), pages 143-164, March.
    10. Baoline Chen, 2012. "A Balanced System of U.S. Industry Accounts and Distribution of the Aggregate Statistical Discrepancy by Industry," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 202-211, February.
    11. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, March.
    12. Víctor Guerrero & Fabio Nieto, 1999. "Temporal and contemporaneous disaggregation of multiple economic time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 459-489, December.
    13. 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.
    14. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    15. Marco Marini & Tommaso Di Fonzo, 2012. "On the Extrapolation with the Denton Proportional Benchmarking Method," IMF Working Papers 2012/169, International Monetary Fund.
    16. Manik L. Shrestha & Marco Marini, 2013. "Quarterly GDP Revisions in G-20 Countries; Evidence from the 2008 Financial Crisis," IMF Working Papers 2013/060, International Monetary Fund.
    17. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    18. Rossi, Nicola, 1982. "A Note on the Estimation of Disaggregate Time Series When the Aggregate Is Known," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 695-696, November.
    19. Marco Marini, 2016. "Nowcasting Annual National Accounts with Quarterly Indicators; An Assessment of Widely Used Benchmarking Methods," IMF Working Papers 2016/071, International Monetary Fund.
    20. Reinier Bikker & Jacco Daalmans & Nino Mushkudiani, 2013. "Benchmarking Large Accounting Frameworks: A Generalized Multivariate Model," Economic Systems Research, Taylor & Francis Journals, vol. 25(4), pages 390-408, December.
    21. van der Ploeg, Frederick, 1985. "Econometrics and inconsistencies in the national accounts," Economic Modelling, Elsevier, vol. 2(1), pages 8-16, January.
    22. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.
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