LASSO-Driven Inference in Time and Space
Wolfgang K. H\"ardle,
Chen Huang and
Papers from arXiv.org
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 regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable selection purpose, 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.
New Economics Papers: this item is included in nep-ecm
Date: 2018-06, Revised 2019-04
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Working Paper: LASSO-Driven Inference in Time and Space (2018)
Working Paper: LASSO-driven inference in time and space (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1806.05081
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