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ITER A quarterly indicator of regional economic activity in Italy

Author

Listed:
  • Valter Di Giacinto

    (Bank of Italy)

  • Libero Monteforte

    (Bank of Italy and Ufficio Parlamentare di Bilancio)

  • Andrea Filippone

    (Bank of Italy)

  • Francesco Montaruli

    (Bank of Italy)

  • Tiziano Ropele

    (Bank of Italy)

Abstract

This work documents the construction of the new quarterly indicator of regional economic activity (Indicatore Trimestrale dell�Economia Regionale � ITER), which uses a parsimonious set of regional variables and combines them by means of temporal disaggregation techniques to obtain a quarterly index that is consistent with the official data on national and regional GDP and marked by a small lag compared with the reference period. The methodology was implemented to produce quarterly indicators for the economies of Italy�s four macro-areas in the period 1995-2017. With a view to assessing the performance of the quarterly indicator, a forecasting exercise was conducted regarding annual GDP growth in the four macro-areas for the period 2014-17. The forecasting performance of ITER is in line with that of the indicators developed by other national research institutions.

Suggested Citation

  • Valter Di Giacinto & Libero Monteforte & Andrea Filippone & Francesco Montaruli & Tiziano Ropele, 2019. "ITER A quarterly indicator of regional economic activity in Italy," Questioni di Economia e Finanza (Occasional Papers) 489, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_489_19
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    6. Carmine Pappalardo & Paolo Piselli, 2002. "Stime ed analisi del ciclo economico territoriale 1987-2000," Rivista di Politica Economica, SIPI Spa, vol. 92(3), pages 201-248, May-June.
    7. 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.
    8. 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.
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    Cited by:

    1. Severin Reissl & Alessandro Caiani & Francesco Lamperti & Tommaso Ferraresi & Leonardo Ghezzi, 2024. "A regional input-output model of the COVID-19 crisis in Italy: decomposing demand and supply factors," Economic Systems Research, Taylor & Francis Journals, vol. 36(1), pages 100-130, January.
    2. Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    temporal disaggregation by related series; regional economies benchmarking and extrapolation; real time estimates;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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