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

Listed author(s):
  • Bialowolski, Piotr
  • Kuszewski, Tomasz
  • Witkowski, Bartosz

The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and dynamic factor models (DFM). Two methods for regressor selection were applied within the BACE framework: frequentist averaging (BA) and frequentist (BF) with a collinearity-corrected version of the latter (BFC). Since models yielded multiple forecasts for each period, an approach to combine them was implemented. Results were assessed using in- and out-of-sample prediction errors. Although results did not vary significantly, best performance was observed from Bayesian models adopting the frequentist approach. Forecast of the unemployment rate were generated with the highest precision, followed by rate of GDP growth and CPI. It can be concluded that although these methods are atheoretical, they provide reasonable forecast accuracy, no worse to that expected from structural models. A further advantage to this approach is that much of the forecast procedure can be automated and much influence from subjective decisions avoided.

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File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2015-31
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File URL: https://www.econstor.eu/bitstream/10419/121391/1/837487056.pdf
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Article provided by Kiel Institute for the World Economy (IfW) in its journal Economics: The Open-Access, Open-Assessment E-Journal.

Volume (Year): 9 (2015)
Issue (Month): ()
Pages: 1-37

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Handle: RePEc:zbw:ifweej:201531
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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. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  3. Clements, Michael P. & Hendry, David F. (ed.), 2011. "The Oxford Handbook of Economic Forecasting," OUP Catalogue, Oxford University Press, number 9780195398649, April.
  4. Daniel Kaufmann & Rolf Scheufele, 2015. "Business Tendency Surveys and Macroeconomic Fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
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