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

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  • Lui, Silvia
  • Mitchell, James
  • Weale, Martin

Abstract

Qualitative expectational data from business surveys are widely used to construct forecasts. However, based typically on evaluation at the macroeconomic level, doubts persist about the utility of these data. This paper evaluates the ability of the underlying firm-level expectations to anticipate subsequent outcomes. Importantly, this evaluation is not hampered by only having access to qualitative outcome data obtained from subsequent business surveys. Quantitative outcome data are also exploited. This required access to a unique panel dataset which matches firms' responses from the qualitative business survey with the same firms' quantitative replies to a different survey carried out by the national statistical office. Nonparametric tests then reveal an apparent paradox. Despite evidence that the qualitative and quantitative outcome data are related, we find that the expectational data offer rational forecasts of the qualitative but not the quantitative outcomes. We discuss the role of "discretisation" errors and the loss function in explaining this paradox.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 4 (October)
Pages: 1128-1146

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Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1128-1146

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Expectational data Qualitative business survey data Firm-level comparison Early indicators Matched data set;

References

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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.

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