Local projection inference in high dimensions

TY - JOUR

T1 - Local projection inference in high dimensions

AU - Adamek, Robert

AU - Smeekes, Stephan

AU - Wilms, Ines

PY - 2024/9/1

Y1 - 2024/9/1

N2 - In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending.

AB - In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending.

KW - high-dimensional data

KW - honest inference

KW - impulse response analysis

KW - lasso

KW - Local projections

UR - http://www.scopus.com/inward/record.url?scp=85207002734&partnerID=8YFLogxK

U2 - 10.1093/ectj/utae012

DO - 10.1093/ectj/utae012

M3 - Journal article

AN - SCOPUS:85207002734

SN - 1368-4221

VL - 27

SP - 323

EP - 342

JO - Econometrics Journal

JF - Econometrics Journal

IS - 3

ER -