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Forecasting manufacturing output growth using firm-level survey data

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  • Dr Martin Weale
  • Dr. James Mitchell

Abstract

Traditionally forecasts of macroeconomic aggregates are extracted from prospective qualitative survey data by relating official data on the aggregate to both the proportion of survey respondents who are "optimists" and the proportion who are "pessimists". But there is no reason to focus on these proportions to the exclusion of other possible means of aggregating and quantifying the underlying panel of respondent or firm-level survey responses. Accordingly in this paper we show how the panel of firm-level responses underlying these proportions can be exploited to derive forecasts of (aggregate) manufacturing output growth that do not lose information that may be contained in the pattern of individual responses. An application using firm-level prospective survey data from the Confederation of British Industry shows that the forecasts of manufacturing output growth derived using these "disaggregate" methods mark an improvement over the so-called "aggregate" methods based on use of the proportions data alone.

Suggested Citation

  • Dr Martin Weale & Dr. James Mitchell, 2005. "Forecasting manufacturing output growth using firm-level survey data," National Institute of Economic and Social Research (NIESR) Discussion Papers 251, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:251
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    References listed on IDEAS

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    Cited by:

    1. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    2. P�r Österholm, 2014. "Survey data and short-term forecasts of Swedish GDP growth," Applied Economics Letters, Taylor & Francis Journals, vol. 21(2), pages 135-139, January.
    3. 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.
    4. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    5. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    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. 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.
    8. Maurizio Bovi, 2006. "Consumers Sentiment and Cognitive Macroeconometrics Paradoxes and Explanations," ISAE Working Papers 66, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    9. François Hild, 2006. "Un nouvel indicateur synthétique prenant en compte la dynamique des réponses individuelles à l'enquête Industrie," Économie et Statistique, Programme National Persée, vol. 395(1), pages 65-89.
    10. 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.
    11. Driver, Ciaran & Muñoz-Bugarin, Jair, 2019. "Financial constraints on investment: Effects of firm size and the financial crisis," Research in International Business and Finance, Elsevier, vol. 47(C), pages 441-457.
    12. 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.
    13. 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.
    14. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    15. Olivier Biau & Hélène Erkel-Rousse & Nicolas Ferrari, 2006. "Réponses individuelles aux enquêtes de conjoncture et prévision de la production manufacturière," Économie et Statistique, Programme National Persée, vol. 395(1), pages 91-116.

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