Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/208240 
Year of Publication: 
2018
Series/Report no.: 
ECB Working Paper No. 2206
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
Abstract: 
Temporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility of OLS estimation, but the consequences have never been properly studied in the mixed frequency context. In this paper, we show, analytically, in Monte Carlo simulations and in a forecasting application on U.S. macroeconomic variables, the relevance of considering the MA component in mixed-frequency MIDAS and Unrestricted-MIDAS models (MIDAS-ARMA and UMIDAS-ARMA). Specifically, the simulation results indicate that the short-term forecasting performance of MIDAS-ARMA and UMIDAS-ARMA is better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting U.S. GDP growth, investment growth and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS-ARMA is better than UMIDAS-ARMA.
Subjects: 
Temporal aggregation
MIDAS models
ARMA models
JEL: 
E37
C53
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-3311-7
Document Type: 
Working Paper

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