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

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Author Info
Hansson, Jesper (National Institute of Economic Research)
Jansson, Per (National Institute of Economic Research)
Löf, Mårten (National Institute of Economic Research)

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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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Publisher 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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Related research
Keywords: Business survey data; Dynamic factor models; Macroeconomic forecasting;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November. [Downloadable!] (restricted)
    Other versions:
  2. The Directorate-General for Economic and Financial Affairs., 2001. "Evaluation and Development of Confidence Indicators Based on Harmonised Business Consumer Surveys," European Economy - Economic Papers 151, Commission of the EC, Directorate-General for Economic and Financial Affairs (DG ECFIN).
  3. 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. [Downloadable!]
  4. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
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  5. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September. [Downloadable!] (restricted)
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  6. Giancarlo Bruno & Marco Malgarini, 2002. "An Indicator of Economic Sentiment for the Italian Economy," ISAE Working Papers 28, ISAE - Institute for Studies and Economic Analyses - (Rome, ITALY). [Downloadable!]
  7. 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. [Downloadable!] (restricted)
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  8. Lindström, Tomas, 2000. "Qualitative Survey Responses and Production over the Business Cycle," Working Paper Series 116, Sveriges Riksbank (Central Bank of Sweden). [Downloadable!]
  9. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc. [Downloadable!]
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  10. 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.
  11. 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. [Downloadable!] (restricted)
  12. Shin-ichi Fukuda & Takashi Onodera, 2001. "A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance? ," CIRJE F-Series CIRJE-F-101, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Christophe Van Nieuwenhuyze, 2006. "A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and GDP growth forecasts," Research series 200603-2, National Bank of Belgium. [Downloadable!]
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