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Forecasting Manufacturing Output Growth Using Firm-Level Survey Data

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  • JAMES MITCHELL
  • RICHARD J. SMITH
  • MARTIN R. WEALE

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. Copyright Blackwell Publishing Ltd and The Victoria University of Manchester, 2005..

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Bibliographic Info

Article provided by University of Manchester in its journal The Manchester School.

Volume (Year): 73 (2005)
Issue (Month): 4 (07)
Pages: 479-499

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Handle: RePEc:bla:manchs:v:73:y:2005:i:4:p:479-499

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Cited by:
  1. 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.
  2. 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.
  3. Dr Silvia Lui & Dr Martin Weale & Dr. James Mitchell, 2008. "Qualitative Business Surveys: Signal or Noise?," NIESR Discussion Papers 1960, National Institute of Economic and Social Research.
  4. 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.
  5. François Hild, 2007. "A New Synthetic Indicator Taking Into Account the Dynamics of Individual Responses to the French Industry Survey," Economie et Statistique, Institut National de la Statistique et des Etudes Economiques, vol. 395, pages 65-89, January.
  6. David Bywaters & Gareth Thomas, 2008. "Output Expectations and Forecasting of UK Manufacturing," Atlantic Economic Journal, International Atlantic Economic Society, vol. 36(2), pages 125-137, June.
  7. 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.
  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. Olivier Biau & Hélène Erkel-Rousse & Nicolas Ferrari, 2007. "Individual Responses to Business Tendency Surveys and the Forecasting of Manufacturing Production," Economie et Statistique, Institut National de la Statistique et des Etudes Economiques, vol. 395, pages 91-116, January.
  10. Troy Matheson & James Mitchell & Brian Silverstone, 2007. "Nowcasting and predicting data revisions in real time using qualitative panel survey data," Reserve Bank of New Zealand Discussion Paper Series DP2007/02, Reserve Bank of New Zealand.

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