Arbeitspapier

LASSO-Driven Inference in Time and Space

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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.

Language: Englisch

Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2018-021

Classification: Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
Forecasting Models; Simulation Methods

Subject: LASSO
time series
simultaneous inference
system of equations
Z-estimation
Bahadur representation
martingale decomposition

Event: Geistige Schöpfung

(who): Chernozhukov, Victor
Härdle, Wolfgang Karl
Huang, Chen
Wang, Weining

Event: Veröffentlichung

(who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

(where): Berlin

(when): 2018

Handle: http://hdl.handle.net/10419/230732

Last update: 10.03.2025, 11:44 AM CET

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Object type

Arbeitspapier

Associated

Chernozhukov, Victor
Härdle, Wolfgang Karl
Huang, Chen
Wang, Weining
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Time of origin

2018

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