Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/189753
Authors: 
Chernozhukov, Victor
Härdle, Wolfgang
Huang, Chen
Wang, Weining
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP36/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.
Subjects: 
LASSO
time series
simultaneous inference
system of equations
Z-estimation
Bahadur representation
martingale decomposition
JEL: 
C12
C22
C51
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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