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LASSO-Driven Inference in Time and Space

Victor Chernozhukov, Wolfgang Härdle (), C. Huang and W. Wang

Working Papers from Department of Economics, City University 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.

Keywords: LASSO; time series; simultaneous inference; system of equations; Z-estimation; Bahadur representation; martingale decomposition (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2018
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http://openaccess.city.ac.uk/20231/1/Wang%20-%20Economics-DP-18_04.pdf

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Working Paper: LASSO-Driven Inference in Time and Space (2018) Downloads
Working Paper: LASSO-driven inference in time and space (2018) Downloads
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