Jurnal Article

Bayesian Averaging vs. Dynamic Factor Models for Forecasting Economic Aggregates with Tendency Survey Data

Piotr Bialowolski, Tomasz Kuszewski, and Bartosz Witkowski

Abstract

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.

JEL C10 C38 C83 E32 E37

Keywords Bayesian averaging of classical estimates; dynamic factor models; tendency

survey data; forecasting

sumber :http://www.economics-ejournal.org/economics/journalarticles/2015-31

kesimpulan : Dapat disimpulkan bahwa meskipun metode ini atheoretical, mereka memberikan akurasi perkiraan yang wajar, tidak lebih buruk dengan yang diharapkan dari model struktural. Sebuah keuntungan lebih lanjut untuk pendekatan ini adalah bahwa banyak prosedur perkiraan dapat otomatis dan banyak pengaruh dari keputusan subjektif dihindari.