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Inspecting the cyclical properties of the Italian Manufacturing Business survey data

Author

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  • Tatiana Cesaroni

    (ISAE - Institute for Studies and Economic Analyses)

Abstract

The aim of this paper is to empirically investigate the cyclical features of the main Italian Manufacturing Business Survey indicators using time and frequency domain techniques. In particular, it analyzes the dynamics of each survey variable over time and with respect to different benchmark business cycles. The findings show that important changes have occurred in the periodicity and volatility of Manufacturing Survey data over the years. As expected, the contemporary cross-correlation of each Survey indicator is higher with respect to the industrial production than it is to the GDP cyclical component. Evidence of significant differences in the co-movements between each indicator with respect to GDP and industrial production is found. The cross-spectral analysis seems to reveal the existence of a common periodicity of all cyclical indicators with both the manufacturing and the whole-economy business cycle. This last result confirms the strength of Business Survey data used as short-run policy indicators.

Suggested Citation

  • Tatiana Cesaroni, 2007. "Inspecting the cyclical properties of the Italian Manufacturing Business survey data," ISAE Working Papers 83, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    • Handle: RePEc:isa:wpaper:83
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    Cited by:

    1. Liudmila Kitrar & Tamara Lipkind, 2021. "Development Of Composite Indicators Of Cyclical Response In Business Surveys Considering The Specifics Of The ‘Covid-19 Economy’," HSE Working papers WP BRP 121/STI/2021, National Research University Higher School of Economics.
    2. W. Hölzl & S. Kaniovski & Y. Kaniovski, 2019. "Exploring the dynamics of business survey data using Markov models," Computational Management Science, Springer, vol. 16(4), pages 621-649, October.
    3. Gabriel Caldas Montes & André Almeida, 2017. "Corruption and business confidence: a panel data analysis," Economics Bulletin, AccessEcon, vol. 37(4), pages 2692-2702.
    4. Cesaroni, Tatiana & Maccini, Louis & Malgarini, Marco, 2011. "Business cycle stylized facts and inventory behaviour: New evidence for the Euro area," International Journal of Production Economics, Elsevier, vol. 133(1), pages 12-24, September.
    5. de Bondt, Gabe & Dieden, Heinz Christian & Muzikarova, Sona & Vincze, Istvan, 2013. "Introducing the ECB indicator on euro area industrial new orders," Occasional Paper Series 149, European Central Bank.
    6. André Filipe Guedes Almeida & Gabriel Caldas Montes, 2020. "Effects of crime and violence on business confidence: evidence from Rio de Janeiro," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 47(7), pages 1669-1688, May.
    7. de Bondt, Gabe & Dieden, Heinz Christian & Muzikarova, Sona & Vincze, Istvan, 2014. "Modelling industrial new orders for the euro area," Statistics Paper Series 6, European Central Bank.
    8. Gabe de Bondt & Heinz C. Dieden & Sona Muzikarova & Istvan Vincze, 2013. "Modeling Euro Area Industrial New Orders," EcoMod2013 5663, EcoMod.
    9. Tatiana Cesaroni, 2010. "Estimating potential output using business survey data in a svar framework," Economics Bulletin, AccessEcon, vol. 30(3), pages 2249-2258.
    10. Tatiana Cesaroni & Stefano Iezzi, 2017. "The Predictive Content of Business Survey Indicators: Evidence from SIGE," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 75-104, May.
    11. de Bondt, Gabe J. & Dieden, Heinz C. & Muzikarova, Sona & Vincze, Istvan, 2014. "Modelling industrial new orders," Economic Modelling, Elsevier, vol. 41(C), pages 46-54.
    12. Liudmila Kitrar & Tamara Lipkind, 2021. "Assessment Of GDP Growth After The Corona Crisis Using The Results Of Business And Consumer Surveys," HSE Working papers WP BRP 118/STI/2021, National Research University Higher School of Economics.
    13. Maria Rita Ippoliti & Fabiana Sartor & Luigi Martone, 2021. "Trade surveys: qualitative and quantitative indicators," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(4), pages 75-85, October-D.
    14. Jürgen Bierbaumer & Werner Hölzl, 2015. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    15. G. Bruno & L. Crosilla & P. Margani, 2019. "Inspecting the Relationship Between Business Confidence and Industrial Production: Evidence on Italian Survey Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(1), pages 1-24, April.
    16. Jochen Hartwig & Sascha Keil, 2025. "The Role of Inventories in European Business Cycles: Evidence from 1999-2023," Chemnitz Economic Papers 066, Department of Economics, Chemnitz University of Technology.
    17. de Bondt, Gabe & Dieden, Heinz Christian & Muzikarova, Sona & Vincze, Istvan, 2013. "Introducing the ECB indicator on euro area industrial new orders," Occasional Paper Series 149, European Central Bank.

    More about this item

    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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