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The cyclical behavior of the Italian business survey data

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  • Tatiana Cesaroni

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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.
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Suggested Citation

  • Tatiana Cesaroni, 2011. "The cyclical behavior of the Italian business survey data," Empirical Economics, Springer, vol. 41(3), pages 747-768, December.
  • Handle: RePEc:spr:empeco:v:41:y:2011:i:3:p:747-768
    DOI: 10.1007/s00181-010-0390-7
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    1. repec:ebl:ecbull:eb-17-00725 is not listed on IDEAS
    2. 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.
    3. 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.
    4. Gabe de Bondt & Heinz C. Dieden & Sona Muzikarova & Istvan Vincze, 2013. "Modeling Euro Area Industrial New Orders," EcoMod2013 5663, EcoMod.
    5. Tatiana Cesaroni, 2010. "Estimating potential output using business survey data in a svar framework," Economics Bulletin, AccessEcon, vol. 30(3), pages 2249-2258.
    6. 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.
    7. 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.
    8. Jürgen Bierbaumer-Polly & Werner Hölzl, 2016. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    9. 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

    Italian business cycle; Spectral analysis; Business survey data; Predictive content; Detrending methods; C32; E32;

    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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