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Business Survey Data: Do They Help in Forecasting the Macro Economy?

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Abstract

In this paper we examine whether data from business tendency surveys are useful for forecasting the macro economy in the short run. Our analyses primarily concern the growth rates of real GDP but we also evaluate forecasts of other variables such as unemployment, price and wage inflation, interest rates, and exchange-rate changes. The starting point is a so-called dynamic factor model (DFM), which is used both as a framework for dimension reduction in forecasting and as a procedure for filtering out unimportant idiosyncratic noise in the underlying survey data. In this way, it is possible to model a rather large number of noise-reduced survey variables in a parsimoniously parameterised vector autoregression (VAR). To assess the forecasting performance of the procedure, comparisons are made with VARs that either use the survey variables directly, are based on macro variables only, or use other popular summary indices of economic activity. As concerns forecasts of GDP growth, the procedure turns out to outperform the competing alternatives in most cases. For the other macro variables, the evidence is more mixed, suggesting in particular that there often is little difference between the DFM-based indicators and the popular summary indices of economic activity.

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Bibliographic Info

Paper provided by National Institute of Economic Research in its series Working Paper with number 84.

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Length: 51 pages
Date of creation: 28 May 2003
Date of revision:
Handle: RePEc:hhs:nierwp:0084

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Keywords: Business survey data; Dynamic factor models; Macroeconomic forecasting;

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References

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  1. Marc Hallin & Mario Forni & Marco Lippi & Lucrezia Reichlin, 2003. "Do financial variables help forecasting inflation and real activity in the Euro area ?," ULB Institutional Repository 2013/2123, ULB -- Universite Libre de Bruxelles.
  2. Bruno, Giancarlo & Malgarini, Marco, 2002. "An Indicator of Economic Sentiment for the Italian Economy," MPRA Paper 42331, University Library of Munich, Germany.
  3. Lindström, Tomas, 2000. "Qualitative Survey Responses and Production over the Business Cycle," Working Paper Series 116, Sveriges Riksbank (Central Bank of Sweden).
  4. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  5. Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
  6. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  7. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  8. G. Goldrian & J.D. Lindbauer & G. Nerb & B. Ulrich, 2001. "Evaluation and development of confidence indicators based on harmonised business and consumer surveys (Study contracted to IFO, Munich)," European Economy - Economic Papers 151, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission.
  9. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  10. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  11. n/a, 2002. "Credibility of the Russian Stabilisation Programme in 1995-98," NIESR Discussion Papers 149, National Institute of Economic and Social Research.
  12. Oller, Lars-Erik & Tallbom, Christer, 1996. "Smooth and timely business cycle indicators for noisy Swedish data," International Journal of Forecasting, Elsevier, vol. 12(3), pages 389-402, September.
  13. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
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Cited by:
  1. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
  2. Aleksejs Melihovs & Svetlana Rusakova, 2005. "Short-Term Forecasting of Economic Development in Latvia Using Business and Consumer Survey Data," Working Papers 2005/04, Latvijas Banka.
  3. Lise Pichette, 2012. "Extracting Information from the Business Outlook Survey Using Statistical Approaches," Discussion Papers 12-8, Bank of Canada.
  4. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2010. "Business Survey Data in Forecasting Macroeconomic Indicators with Combined Forecasts," Contemporary Economics, University of Finance and Management in Warsaw, vol. 4(4), December.

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