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The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys

Author

Listed:
  • Dr Silvia Lui

  • Dr Martin Weale

  • Dr. James Mitchell

Abstract

This paper assesses the utility of qualitative expectational survey data at the firm-level in terms of both their ability to anticipate firms' subsequent retrospective, but qualitative, reports of their performance but also these same firms' quantitative answers. The assessment requires access to a unique panel dataset which matches firms' responses to a leading qualitative tendency survey conducted by the Confederation of British Industry with these same firms' quantitative replies to a different survey carried out by the Office for National Statistics. We employ nonparametric tests of the so-called 'best-case scenario' and introduce a weaker test for the coherence between these two surveys and test whether the qualitative data contain a(ny) signal about the quantitative data. We find that while firms' qualitative expectations are 'best-case' predictions of their qualitative assessment of their output growth they do not contain a signal about the quantitative data. But we can reject the null hypothesis of noise for the retrospective qualitative data. We discuss this apparent paradox and suggest that qualitative business survey data are more useful for nowcasting than forecasting.

Suggested Citation

  • Dr Silvia Lui & Dr Martin Weale & Dr. James Mitchell, 2009. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," National Institute of Economic and Social Research (NIESR) Discussion Papers 343, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:343
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    2. 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.
    3. Kevin Lee & Michael Mahony & Paul Mizen, 2020. "The CBI Suite of Business Surveys," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-08, Economic Statistics Centre of Excellence (ESCoE).
    4. 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.
    5. 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.
    6. 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.
    7. Lena Boneva & James Cloyne & Martin Weale & Tomasz Wieladek, 2020. "Firms' Price, Cost and Activity Expectations: Evidence from Micro Data," The Economic Journal, Royal Economic Society, vol. 130(627), pages 555-586.
    8. 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.
    9. Boneva, Lena & Cloyne, James & Weale, Martin & Wieladek, Tomasz, 2018. "Firms' Expectations of New Orders, Employment, Costs and Prices: Evidence from Micro Data," CEPR Discussion Papers 12722, C.E.P.R. Discussion Papers.
    10. Puah, Chin-Hong & Wong, Shirly Siew-Ling & Habibullah, Muzafar Shah, 2012. "Rationality of business operational forecasts: evidence from Malaysian distributive trade sector," MPRA Paper 37599, University Library of Munich, Germany.
    11. Lucia Modugno, 2024. "Evaluating Qualitative Expectational Data on Investments from Business Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(1), pages 59-88, August.
    12. 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.
    13. Das, Abhiman & Lahiri, Kajal & Zhao, Yongchen, 2019. "Inflation expectations in India: Learning from household tendency surveys," International Journal of Forecasting, Elsevier, vol. 35(3), pages 980-993.
    14. 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.
    15. Antonecchia, Gianluca, 2023. "Heterogeneous expectations, forecast accuracy and firms’ credit demand," European Economic Review, Elsevier, vol. 154(C).
    16. 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.
    17. Maria Rita Ippoliti & Luigi Martone & Fabiana Sartor, 2024. "Building an integrated database for the trade sector for the period 2010- 2022," 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. 78(1), pages 75-84, January-M.
    18. Ferrando, Annalisa & Ganoulis, Ioannis & Preuss, Carsten, 2019. "Firms’ expectations on the availability of credit since the financial crisis," Working Paper Series 2341, European Central Bank.
    19. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    20. Pignataro, Giuseppe & Raggi, Davide & Pancotto, Francesca, 2024. "On the role of fundamentals, private signals, and beauty contests to predict exchange rates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 687-705.
    21. Alex Botsis & Kevin Lee, 2022. "Nowcasting Using Firm-Level Survey Data; Tracking UK Output Fluctuations and Recessionary Events," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-20, Economic Statistics Centre of Excellence (ESCoE).
    22. Martinsen, Kjetil & Ravazzolo, Francesco & Wulfsberg, Fredrik, 2014. "Forecasting macroeconomic variables using disaggregate survey data," International Journal of Forecasting, Elsevier, vol. 30(1), pages 65-77.

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