Inference for High-Dimensional Local Projection

Jiti Gao (), Fei Liu () and Bin Peng ()

No 1/26, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper rigorously analyzes the properties of the local projection methodology within a high-dimensional framework, with a central focus on achieving robust long-horizon inference. We integrate a general dependence structure into h-step ahead forecasting models via a flexible specification of the residual terms. Additionally, we study the corresponding high-dimensional covariance matrix estimation, explicitly addressing the complexity arising from the long-horizon setting. Extensive Monte Carlo simulations are conducted to substantiate the derived theoretical findings. In the empirical study, we utilize the proposed high-dimensional local projection framework to study the impact of business news attention on U.S. industry-level stock volatility.

Keywords: high-dimensional local projection; long-horizon analysis; h-step ahead forecasting models; covariance matrix estimation; high-dimensional time series; volatility spillovers (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 (search for similar items in EconPapers)
Pages: 34
Date: 2026
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