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Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data

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  • Bialowolski, Piotr
  • Kuszewski, Tomasz
  • Witkowski, Bartosz

Abstract

The main goal of the article is to investigate forecasting quality of two approaches to modelling main macroeconomic variables without a priori assumptions concerning causality and generate forecasts without additional assumptions regarding regressors. With application of tendency survey data the authors develop methodology for application of the Bayesian averaging of classical estimates (BACE) but also construct dynamic factor models (DFM). Within the BACE framework they apply two diversified methods of regressors' selection: frequentist (FMA) and averaging (BMA). Because their models yield multiple forecasts for each period, subsequently the authors employ diversified approaches to combine forecasts. The assessment of the results is performed with in-sample and out-of-sample prediction errors. Although the results do not significantly differ, the best performance is observed in Bayesian models with frequentist approach. Their analysis conducted for Polish economy also shows that the unemployment rate turns out to be forecasted with highest precision, followed by the rate of GDP growth and the CPI. It can be concluded from their analyses that although their methods are atheoretical they provide reasonable forecast accuracy not inferior to that of structural models. Additional advantage of their approach is that the forecasting procedure can be mostly automated and the influence of subjective decisions made in the forecasting process can be significantly reduced.

Suggested Citation

  • Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics Discussion Papers 2015-28, Kiel Institute for the World Economy (IfW Kiel).
  • Handle: RePEc:zbw:ifwedp:201528
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    1. Ronald L. Cooper, 1972. "The Predictive Performance of Quarterly Econometric Models of the United States," NBER Chapters, in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 813-947, National Bureau of Economic Research, Inc.
    2. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2010. "Survey data as coincident or leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 109-131.
    3. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic Factor Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 3, pages 25-40, Springer.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Marcin Kolasa & MichaŁ Rubaszek & PaweŁ SkrzypczyŃski, 2012. "Putting the New Keynesian DSGE Model to the Real-Time Forecasting Test," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1301-1324, October.
    6. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Clements, Michael P. & Hendry, David F. (ed.), 2011. "The Oxford Handbook of Economic Forecasting," OUP Catalogue, Oxford University Press, number 9780195398649.
    9. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    10. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
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    Cited by:

    1. Martin Feldkircher & Florian Huber & Josef Schreiner & Marcel Tirpák & Peter Tóth & Julia Wörz, 2015. "Bridging the information gap: small-scale nowcasting models of GDP growth for selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 56-75.

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    More about this item

    Keywords

    Bayesian averaging of classical estimates; dynamic factor models; tendency survey data; forecasting;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling 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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