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Indirect estimation of the monthly transport turnover indicator in Italy

Author

Listed:
  • Barbara Guardabascio

    (University of Perugia)

  • Filippo Moauro

    (ISTAT)

  • Luke Mosley

    (University of Lancaster)

Abstract

The paper discusses the results of a selection of a set of monthly indicators to be used as predictors of the quarterly index of Italian service turnover. A mixed frequency approach based on sparse temporal disaggregation is used, which outperforms the classical methods of the Chow and Lin family, allowing both a high number of regressors by the LASSO method and stable estimates. The application refers to the turnover in transport, a sector strongly affected in 2020 by the dramatic movements due to the COVID-19 pandemic and the resurgence of inflation at the end of 2021. The monthly indicators are selected from 143 time series: 56 series of business surveys in transport about both the climate and frequency of the answers; 18 series from Assaeroporti about both passengers and cargo flights split by national and international routes; 69 series of monthly turnover in industry split by both sector of economic activity and reference market. The sample spans the months from January 2010 to December 2021 for both seasonally adjusted and unadjusted data. Several aspects of the estimation are considered: the stability of selected indicators over the quarters 2017–2021; their forecasting performance; the reliability of the estimates in terms of their monthly pattern.

Suggested Citation

  • Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
    • Handle: RePEc:spr:empeco:v:67:y:2024:i:2:d:10.1007_s00181-024-02571-6
    DOI: 10.1007/s00181-024-02571-6
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    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Alberto Alesina & Silvia Ardagna & Giuseppe Nicoletti & Fabio Schiantarelli, 2005. "Regulation And Investment," Journal of the European Economic Association, MIT Press, vol. 3(4), pages 791-825, June.
    3. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 439-470, April.
    4. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    5. Ron S. Jarmin, 2019. "Evolving Measurement for an Evolving Economy: Thoughts on 21st Century US Economic Statistics," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 165-184, Winter.
    6. A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.
    7. Edward L. Glaeser & Janet E. Kohlhase, 2004. "Cities, regions and the decline of transport costs," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 197-228, Springer.
    8. 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.
    9. Guglielmo Barone & Federico Cingano, 2011. "Service Regulation and Growth: Evidence from OECD Countries," Economic Journal, Royal Economic Society, vol. 121(555), pages 931-957, September.
    10. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, July.
    11. 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.
    12. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    13. 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.
    14. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
    15. 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.
    16. 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.
    17. Saboori, Behnaz & Sapri, Maimunah & bin Baba, Maizan, 2014. "Economic growth, energy consumption and CO2 emissions in OECD (Organization for Economic Co-operation and Development)'s transport sector: A fully modified bi-directional relationship approach," Energy, Elsevier, vol. 66(C), pages 150-161.
    18. Joseph Berechman & Dilruba Ozmen & Kaan Ozbay, 2006. "Empirical analysis of transportation investment and economic development at state, county and municipality levels," Transportation, Springer, vol. 33(6), pages 537-551, November.
    19. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    20. Paul Labonne & Martin Weale, 2020. "Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value‐added tax data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1211-1230, June.
    21. Peter Oberhofer & Maria Dieplinger, 2014. "Sustainability in the Transport and Logistics Sector: Lacking Environmental Measures," Business Strategy and the Environment, Wiley Blackwell, vol. 23(4), pages 236-253, May.
    22. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
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