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Quantification of qualitative firm-level survey data

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  • Dr Martin Weale

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  • Dr. James Mitchell

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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:2136
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    References listed on IDEAS

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    1. Simon Wren-Lewis, 1985. "The Quantification of Survey Data on Expectations," National Institute Economic Review, National Institute of Economic and Social Research, vol. 113(1), pages 39-49, August.
    2. Smith, Jeremy & McAleer, Michael, 1995. "Alternative Procedures for Converting Qualitative Response Data to Quantitative Expectations: An Application to Australian Manufacturing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 165-185, April-Jun.
    3. Breusch, T S, 1978. "Testing for Autocorrelation in Dynamic Linear Models," Australian Economic Papers, Wiley Blackwell, vol. 17(31), pages 334-355, December.
    4. (ed.), 1992. "Index," Books, Edward Elgar Publishing, number 1241.
    5. Pesaran, M.H., 1992. "A Generalised R2 Criterion for Regression Models Estimated by the Instrumental Variable Method," Cambridge Working Papers in Economics 9220, Faculty of Economics, University of Cambridge.
    6. Richard Stone & D. G. Champernowne & J. E. Meade, 1942. "The Precision of National Income Estimates," Review of Economic Studies, Oxford University Press, vol. 9(2), pages 111-125.
    7. Nerlove, Marc, 1983. "Expectations, Plans, and Realizations in Theory and Practice," Econometrica, Econometric Society, vol. 51(5), pages 1251-1279, September.
    8. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
    9. Smith, Richard J, 1989. "On the Use of Distributional Mis-specification Checks in Limited Dependent Variable Models," Economic Journal, Royal Economic Society, vol. 99(395), pages 178-192, Supplemen.
    10. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, November.
    11. Chambers, Ray & Weale, Martin & Youll, Robin, 2000. "The Average Earnings Index," Economic Journal, Royal Economic Society, vol. 110(461), pages 100-121, February.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Tatiana Cesaroni, 2011. "The cyclical behavior of the Italian business survey data," Empirical Economics, Springer, vol. 41(3), pages 747-768, December.
    4. 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.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    6. repec:spr:soinre:v:135:y:2018:i:1:d:10.1007_s11205-016-1490-3 is not listed on IDEAS
    7. 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.
    8. 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.
    9. Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," MPRA Paper 42332, University Library of Munich, Germany.
    10. 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.
    11. 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.
    12. Miguel A. Costa-Gomes & Georg Weizsäcker, 2008. "Stated Beliefs and Play in Normal-Form Games," Review of Economic Studies, Oxford University Press, vol. 75(3), pages 729-762.
    13. 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.
    14. 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.
    15. repec:spr:qualqt:v:51:y:2017:i:6:d:10.1007_s11135-016-0416-0 is not listed on IDEAS
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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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