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Nowcasting and predicting data revisions in real time using qualitative panel survey data

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Abstract

The qualitative responses that firms give to business survey questions regarding changes in their own output provide a real-time signal of official output changes. The most commonly-used method to produce an aggregate quantitative indicator from business survey responses - the net balance, or diffusion index - has changed little in 40 years. It focuses on the proportion of survey respondents replying "up", "the same" or "down". This paper investigates whether an improved real-time signal of official output data changes can be derived from a recently advanced method on the aggregation of survey data from panel responses. It also considers the ability of survey data to anticipate revisions to official output data. We find, in a New Zealand application, that exploiting the panel dimension to qualitative survey data gives a better in-sample signal about official data than traditional methods. This is achieved by giving a higher weight to firms whose answers have a close link to official data than to those whose experiences correspond only weakly or not at all. Out-of-sample, it is less clear it matters how survey data are quantified with simpler and more parsimonious methods hard to improve. It is clear, nevertheless, that survey data, exploited in some form, help to explain revisions to official data.

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Paper provided by Reserve Bank of New Zealand in its series Reserve Bank of New Zealand Discussion Paper Series with number DP2007/02.

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Length: 25 p.
Date of creation: Feb 2007
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Handle: RePEc:nzb:nzbdps:2007/02

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  1. James Mitchell & Richard J. Smith & Martin R. Weale, 2005. "Forecasting Manufacturing Output Growth Using Firm-Level Survey Data," Manchester School, University of Manchester, vol. 73(4), pages 479-499, 07.
  2. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-38, May.
  3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, April.
  4. Michela Nardo, 2003. "The Quantification of Qualitative Survey Data : A Critical Assessment," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 645-668, December.
  5. 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, 02.
  6. Jon Faust & John H. Rogers & Jonathan H. Wright, 2000. "News and noise in G-7 GDP announcements," International Finance Discussion Papers 690, Board of Governors of the Federal Reserve System (U.S.).
  7. M. Hashem Pesaran & Martin Weale, 2005. "Survey Expectations," CESifo Working Paper Series 1599, CESifo Group Munich.
  8. Elliott, Graham & Timmermann, Allan G, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
  9. Cath Sleeman, 2006. "Analysis of revisions to quarterly GDP - a real-time database," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 69, pages 44p., March.
  10. 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 165-85, April-Jun.
  11. Ray Barrell, 1999. "Employment Security and European Labour Demand: A Panel Study Across 16 Industries," NIESR Discussion Papers 194, National Institute of Economic and Social Research.
  12. Tom Stark, 2002. "A summary of the conference on real-time data analysis," Business Review, Federal Reserve Bank of Philadelphia, issue Q 1, pages 5-11.
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Cited by:
  1. Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.

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