In this paper, a generalized dynamic factor model is utilized to produce short-term forecasts of real Austrian GDP. The model follows the frequency domain approach proposed by Forni, Hallin, Lippi and Reichlin (2000, 2003). The forecasting performance of the model with a large data set of 143 variables has been assessed relative to simple univariate time-series forecasts. The results show that the factor model can barely outperform the much simpler benchmark model, given the usuall levels of significance. Thus we followed a line of research proposed by Boivin and Ng (2003) and Watson (2000), who suggested that the use of a small data set may increase the forecasting performance. The main finding from our extensive out-of-sample forecasting experiment that we have conducted is that the best forecasting performance can be achieved with small data sets with a handful of variables only. These models perform signifi- cantly better than the large model. This result seems to contradict the basic idea of dynamic factor models, which have been constructed to exploit the potentially useful information of a large data set.
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Paper provided by Oesterreichische Nationalbank (Austrian Central Bank) in its series Working Papers with number
89.
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.:
Artis, Michael J & Banerjee, Anindya & Marcellino, Massimiliano, 2002.
"Factor Forecasts for the UK,"
CEPR Discussion Papers
3119, C.E.P.R. Discussion Papers.
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Michael Artis & Anindya Banerjee & Massimiliano Marcellino, .
"Factor forecasts for the UK,"
Working Papers
203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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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.)