Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model
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This paper proposes a mixed-frequency small open economy structural model, in which the structure comes from a New Keynesian dynamic stochastic general equilibrium (DSGE) model. An aggregation rule is proposed to link the latent aggregator to the observed quarterly output growth via aggregation. The resulting state-space model is estimated by the Kalman filter and the estimated current aggregator is used to nowcast the quarterly GDP growth. Taiwanese data from January 1998 to December 2015 are used to illustrate how to implement the technique. The DSGE-based mixed-frequency model outperforms the reduced-form mixed-frequency model and the MIDAS model on nowcasting Taiwan’s quarterly GDP growth.
KeywordsDSGE model Mixed frequency Nowcasting Kalman filter
JEL ClassificationC5 E1
The authors are grateful for helpful comments from Kenneth West, Barbara Rossi, Frédérique Bec, Yu-Ning Huang, Yi-Ting Chen, and participants at the 2016 International Symposium in Computational Economics and Finance (ISCEF) in Paris.
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