book

Thematic Sessions
ST1: Data Science
Chair: Marcelo Medeiros
Wednesday, September 4th
10:30
An Automated Approach Towards Sparse
Single-Equation Cointegration Modelling
Etienne Wijler
Maastricht University – Netherlands
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 an appropriate linear combination of the cointegrating vectors
that may occur in the underlying DGP, while simultaneously enabling the correct recovery of
sparsity patterns in the corresponding parameter space. A simulation study shows strong selective
capabilities, as well as superior predictive performance in the context of nowcasting compared
to high-dimensional models that ignore cointegration. An empirical application to nowcasting
Dutch unemployment rates using Google Trends confirms the strong practical performance of our
procedure.
Keywords: SPECS; Penalized Regression; Single-Equation Error-Correction Model; Cointegration;
High-Dimensional Data
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