B0570
Title: Sparse single-equation error correction models in high dimensions
Authors: Stephan Smeekes - Maastricht University (Netherlands)
Etienne Wijler - Maastricht University (Netherlands) [presenting]
Abstract: In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. We show that SPECS is able to consistently estimate a linear combination of the cointegrating vectors, while simultaneously enabling the correct recovery of sparsity patterns in the parameter space. The results are derived in an asymptotic framework where both the number of (ir)relevant variables as well as the number of observations diverge. Novel eigenvalue conditions with broad applicability to penalized regression estimators are presented.
