Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148314
Authors: 
Kurz-Kim, Jeong-Ryeol
Year of Publication: 
2016
Series/Report no.: 
Discussion Paper, Deutsche Bundesbank 47/2016
Abstract: 
Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.
Subjects: 
Factor model
MIDAS
Lasso
Elastic Net
ECM
Nowcasting
Forecasting
JEL: 
C18
C23
C51
C52
C53
ISBN: 
978-3-95729-324-4
Document Type: 
Working Paper

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