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Efficient Aggregation of Panel Qualitative Survey Data

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  • James Mitchell

    ()

  • Richard J. Smith
  • Martin R. Weale

Abstract

Qualitative business survey data are used widely to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report “up” and “down”. This paper examines disaggregate or firm-level survey responses. It considers how the responses of the individual firms should be quantified and combined if the aim is to produce an early indication of official output data. Having linked firms’ categorical responses to official data using ordered discrete choice models, the paper proposes a statistically efficient means of combining the disparate estimates of aggregate output growth which can be constructed from the responses of individual firms. An application to firm-level survey data from the Confederation of British Industry shows that the proposed indicator can provide early estimates of output growth more accurately than traditional indicators.

Suggested Citation

  • James Mitchell & Richard J. Smith & Martin R. Weale, 2011. "Efficient Aggregation of Panel Qualitative Survey Data," Discussion Papers in Economics 11/53, Department of Economics, University of Leicester.
  • Handle: RePEc:lec:leecon:11/53
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    References listed on IDEAS

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    1. Bontemps, Christian & Meddahi, Nour, 2005. "Testing normality: a GMM approach," Journal of Econometrics, Elsevier, vol. 124(1), pages 149-186, January.
    2. Pesaran, M. Hashem & Weale, Martin, 2006. "Survey Expectations," Handbook of Economic Forecasting, Elsevier.
    3. Ciaran Driver & Giovanni Urga, 2004. "Transforming Qualitative Survey Data: Performance Comparisons for the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 71-89, February.
    4. Batchelor, R. A., 1981. "Aggregate expectations under the stable laws," Journal of Econometrics, Elsevier, vol. 16(2), pages 199-210, June.
    5. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    6. James Mitchell & Richard J. Smith & Martin R. Weale, 2005. "Forecasting Manufacturing Output Growth Using Firm-Level Survey Data," Manchester School, University of Manchester, vol. 73(4), pages 479-499, July.
    7. McIntosh, James & Schiantarelli, Fabio & Low, William, 1989. "A Qualitative Response Analysis of UK Firms' Employment and Output Decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(3), pages 251-264, July-Sept.
    8. Ray Barrell, 1999. "Employment Security and European Labour Demand: A Panel Study Across 16 Industries," National Institute of Economic and Social Research (NIESR) Discussion Papers 148, National Institute of Economic and Social Research.
    9. 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.
    10. Chesher, Andrew & Irish, Margaret, 1987. "Residual analysis in the grouped and censored normal linear model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 33-61.
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    Cited by:

    1. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.

    More about this item

    Keywords

    Survey Data; Indicators; Quantification; Forecasting; Forecast Combination;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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