Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230828
Authors: 
Wang, Weining
Yu, Lining
Wang, Bingling
Year of Publication: 
2020
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2020-022
Abstract: 
A factor augmented dynamic model for analysing tail behaviour of high dimensional time series is proposed. As a first step, the tail event driven latent factors are extracted. In the second step, a VAR (Vectorautoregression model) is carried out to analyse the interaction between these factors and the macroeconomic variables. Furthermore, this methodology also provides the possibility for central banks to examine the sensitivity between macroeconomic variables and financial shocks via impulse response analysis. Then the predictability of our estimator is illustrated. Finally, forecast error variance decomposition is carried out to investigate the network effect of these variables. The interesting findings are: firstly, GDP and Unemployment rate are very much sensitive to the shock of financial tail event driven factors, while these factors are more affected by inflation and short term interest rate. Secondly, financial tail event driven factors play important roles in the network constructed by the extracted factors and the macroeconomic variables. Thirdly, there is more connectedness during financial crisis than in the stable periods. Compared with median case, the network is more dense in lower quantile level.
Subjects: 
Quantile Regression
Expectile Regression
Dynamic Factor Model
Dynamic Network
JEL: 
C21
C51
G01
G18
G32
G38
Document Type: 
Working Paper

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