High-dimensional Simultaneous Inference on Non-Gaussian VAR Model via De-biased Estimator

TL;DR

This paper introduces a robust, bootstrap-based method for high-dimensional, non-Gaussian VAR models that enables simultaneous inference on transition coefficients, even with exponentially growing dimensions.

Contribution

It develops a multiplier bootstrap procedure with a de-biased estimator for high-dimensional non-Gaussian VAR models, addressing tail behavior and distributional challenges.

Findings

Method performs well in simulations

Allows exponential growth in dimension

Provides valid inference under mild conditions

Abstract

Simultaneous inference for high-dimensional non-Gaussian time series is always considered to be a challenging problem. Such tasks require not only robust estimation of the coefficients in the random process, but also deriving limiting distribution for a sum of dependent variables. In this paper, we propose a multiplier bootstrap procedure to conduct simultaneous inference for the transition coefficients in high-dimensional non-Gaussian vector autoregressive (VAR) models. This bootstrap-assisted procedure allows the dimension of the time series to grow exponentially fast in the number of observations. As a test statistic, a de-biased estimator is constructed for simultaneous inference. Unlike the traditional de-biased/de-sparsifying Lasso estimator, robust convex loss function and normalizing weight function are exploited to avoid any unfavorable behavior at the tail of the distribution. We develop Gaussian approximation theory for VAR model to derive the asymptotic distribution of the de-biased estimator and propose a multiplier bootstrap-assisted procedure to obtain critical values under very mild moment conditions on the innovations. As an important tool in the convergence analysis of various estimators, we establish a Bernstein-type probabilistic concentration inequality for bounded VAR models. Numerical experiments verify the validity and efficiency of the proposed method.