Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach

2018 ◽  
Vol 43 ◽  
pp. 118-128 ◽  
Author(s):  
Kaiji Motegi ◽  
Akira Sadahiro
Keyword(s):  
Vector Autoregression ◽  
Private Investment ◽  
Frequency Vector ◽  
Mixed Frequency ◽  
Lost Decade

scholarly journals Large mixed-frequency VARs with a parsimonious time-varying parameter structure

2021 ◽  
Author(s):  
Thomas B Götz ◽  
Klemens Hauzenberger
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Computational Complexity ◽  
Common Factor ◽  
Time Varying ◽  
Frequency Vector ◽  
Mixed Frequency ◽  
Forecasting Models ◽  
Time Varying Parameter ◽  
Joint Dynamics

Summary In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

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