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Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts

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  • Bisio, Laura
  • Moauro, Filippo

Abstract

In this paper we discuss the most recent developments of temporal disaggregation techniques carried out at ISTAT. They concern the extension from static to dynamic autoregressive distributed lag ADL regressions and the change to a state-space framework for the statistical treatment of temporal disaggregation. Beyond the development of a unified procedure for both static and dynamic methods from one side and the treatment of the logarithmic transformation from the other, we provide short guidelines for model selection. From the empirical side we evaluate the new dynamic methods by implementing a large scale temporal disaggregation exercise using ISTAT annual value added data jointly with quarterly industrial production by branch of economic activity over the period 1995-2013. The main finding of this application is that ADL models either in levels and logarithms can reduce the errors due to extrapolating disaggregated data in last quarters before the annual benchmarks become available. When the attention moves to the correlations with the high-frequency indicators the ADL disaggregations are also generally in line with those produced by the static Chow-Lin variants, with problematic outcomes limited to few cases.

Suggested Citation

  • Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    • Handle: RePEc:pra:mprapa:80211
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    10. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2010. "Survey data as coincident or leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 109-131.
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    13. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
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    3. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
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    5. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.

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    More about this item

    Keywords

    temporal disaggregation; state-space form; Kalman filter; ADL models; linear Gaussian approximating model; quarterly national accounts.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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