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Forecasting Manufacturing Output Growth Using Firm-Level Survey Data

Listed author(s):
  • JAMES MITCHELL
  • RICHARD J. SMITH
  • MARTIN R. WEALE

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

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

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Handle: RePEc:bla:manchs:v:73:y:2005:i:4:p:479-499
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  1. Entorf, Horst, 1993. "Constructing leading indicators from non-balanced sectoral business survey series," International Journal of Forecasting, Elsevier, vol. 9(2), pages 211-225, August.
  2. 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.
  3. Smith, Jeremy & McAleer, Michael, 1995. "Alternative Procedures for Converting Qualitative Response Data to Quantitative Expectations: An Application to Australian Manufacturing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 165-185, April-Jun.
  4. François Bouton & Hélène Erkel-Rousse, 2002. "Conjonctures sectorielles et prévision à court terme de l'activité : l'apport de l'enquête de conjoncture dans les services," Économie et Statistique, Programme National Persée, vol. 359(1), pages 35-68.
  5. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
  6. Stéphane Grégoir & Fabrice Lenglart, 1998. "Measuring the Probability of a Business Cycle Turning Point by Using a Multivariate Qualitative Hidden Markov Model," Working Papers 98-48, Center for Research in Economics and Statistics.
  7. 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.
  8. William A. Branch, 2004. "The Theory of Rationally Heterogeneous Expectations: Evidence from Survey Data on Inflation Expectations," Economic Journal, Royal Economic Society, vol. 114(497), pages 592-621, July.
  9. Smith, Richard J & Blundell, Richard W, 1986. "An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply," Econometrica, Econometric Society, vol. 54(3), pages 679-685, May.
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