Template-Type: ReDIF-Paper 1.0
Author-Name: Schnücker, A.M.
Author-Name-Last: Schnücker
Author-Name-First: Annika
Title: Penalized Estimation of Panel Vector Autoregressive Models
Abstract: 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 coeficients across panel units, for penalization of lags of
 variables belonging 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 with five
 countries support these findings.
Length: 39
Creation-Date: 2019-11-01
File-URL: https://repub.eur.nl/pub/122072/EI2019-33.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI-2019-33
Classification-JEL: C13, C32, C33
Keywords: Model selection, multi-country model, shrinkage estimation
Handle: RePEc:ems:eureir:122072