Penalized estimation of panel vector autoregressive models: A panel LASSO approach

This paper proposes LASSO estimation specific for panel vector autoregressive (PVAR) models. The penalty term allows for shrinkage for different lags, for shrinkage towards homogeneous coefficients across panel units, for penalization of lags of variables be-longing to another cross-sectional unit, and for varying penalization across equations. The penalty parameters therefore build on time series and cross-sectional properties that are commonly found in PVAR models. Simulation results point towards advantages of using the proposed LASSO for PVAR models over ordinary least squares in terms of forecast accuracy. An empirical forecasting application including 20 countries supports these findings.& COPY; 2022 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper proposes a specific LASSO estimation method for panel vector autoregressive (PVAR) models. The penalty term allows for shrinkage for different lags, homogeneous coefficients across panel units, lags of variables belonging to another cross-sectional unit, and varying penalization across equations. Simulation results suggest that the proposed LASSO for PVAR models outperforms ordinary least squares in terms of forecast accuracy. An empirical forecasting application involving 20 countries provides support for these findings.