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

Author

Listed:
  • Hansson, Jesper

    (National Institute of Economic Research)

  • Jansson, Per

    (Monetary Policy Department, Central Bank of Sweden)

  • Löf, Mårten

    (National Institute of Economic Research)

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.

Suggested Citation

  • Hansson, Jesper & Jansson, Per & Löf, Mårten, 2003. "Business Survey Data: Do They Help in Forecasting the Macro Economy?," Working Paper Series 151, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0151
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Lise Pichette, 2012. "Extracting Information from the Business Outlook Survey Using Statistical Approaches," Discussion Papers 12-8, Bank of Canada.
    3. 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.
    4. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2010. "Business Survey Data in Forecasting Macroeconomic Indicators with Combined Forecasts," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 4(4), December.
    5. Christophe Van Nieuwenhuyze, 2006. "A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and GDP growth forecasts," Working Paper Research 80, National Bank of Belgium.
    6. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2012. "Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 6(1), March.

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    More about this item

    Keywords

    Business survey data; Dynamic factor models; Macroeconomic forecasting;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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