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Quantification of qualitative firm-level survey data

Author

Listed:
  • Dr Martin Weale

  • Dr. James Mitchell

Abstract

Survey data are widely used to provide indicators of economic activity ahead of the publication of official data. This paper proposes an indicator based on a theoretically consistent procedure for quantifying firm-level survey responses that are ordered and categorical. Firms" survey responses are assumed to be triggered by a latent continuous random variable as it crosses thresholds. Breaking tradition these thresholds are not assumed time invariant. An application to firm-level survey data from the Confederation of British Industry shows that the proposed indicator of manufacturing output growth outperforms traditional indicators that assume time-invariant thresholds. Copyright Royal Economic Society 2002.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Dr Martin Weale & Dr. James Mitchell, 2001. "Quantification of qualitative firm-level survey data," National Institute of Economic and Social Research (NIESR) Discussion Papers 181, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:181
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    Cited by:

    1. Bruno Giancarlo & Lupi Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," ISAE Working Papers 33, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. von Kalckreuth, Ulf & Murphy, Emma, 2005. "Financial constraints and capacity adjustment in the United Kingdom: Evidence from a large panel of survey data," Discussion Paper Series 1: Economic Studies 2005,01, Deutsche Bundesbank.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    4. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    5. Tatiana Cesaroni, 2011. "The cyclical behavior of the Italian business survey data," Empirical Economics, Springer, vol. 41(3), pages 747-768, December.
    6. Minkler, Lanse, 2004. "Shirking and motivations in firms: survey evidence on worker attitudes," International Journal of Industrial Organization, Elsevier, vol. 22(6), pages 863-884, June.
    7. Karl Taylor & Robert McNabb, 2007. "Business Cycles and the Role of Confidence: Evidence for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(2), pages 185-208, April.
    8. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "Tracking economic growth by evolving expectations via genetic programming: A two-step approach," Working Papers XREAP2018-4, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    10. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    11. Miguel A. Costa-Gomes & Georg Weizsäcker, 2008. "Stated Beliefs and Play in Normal-Form Games," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(3), pages 729-762.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    13. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    14. Tommaso Proietti & Cecilia Frale, 2011. "New proposals for the quantification of qualitative survey data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(4), pages 393-408, July.
    15. David Bywaters & Gareth Thomas, 2008. "Output Expectations and Forecasting of UK Manufacturing," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 36(2), pages 125-137, June.
    16. 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.
    17. Ulf von Kalckreuth, 2005. "Financial constraints and real activity: a non-structural approach using UK survey data," BIS Papers chapters, in: Bank for International Settlements (ed.), Investigating the relationship between the financial and real economy, volume 22, pages 64-80, Bank for International Settlements.
    18. Silvia Lui & James Mitchell & Martin Weale, 2011. "Qualitative business surveys: signal or noise?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 327-348, April.
    19. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    21. Leandro D�Aurizio & Stefano Iezzi, 2011. "Investment forecasting with business survey data," Temi di discussione (Economic working papers) 832, Bank of Italy, Economic Research and International Relations Area.
    22. Breitung, Jörg & Schmeling, Maik, 2013. "Quantifying survey expectations: What’s wrong with the probability approach?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 142-154.
    23. Kajal Lahiri & Yongchen Zhao, 2013. "Quantifying Heterogeneous Survey Expectations: The Carlson-Parkin Method Revisited," Discussion Papers 13-08, University at Albany, SUNY, Department of Economics.
    24. Nolte, Ingmar & Pohlmeier, Winfried, 2007. "Using forecasts of forecasters to forecast," International Journal of Forecasting, Elsevier, vol. 23(1), pages 15-28.

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