An automated approach towards sparse single-equation cointegration modelling

Stephan Smeekes, Etienne Wijler*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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. Under an asymptotic regime in which both the number of parameters and time series observations jointly diverge to infinity, we show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP. In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the parameter space and to possess the same limiting distribution as the OLS oracle procedure. 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. (c) 2020 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)247-276
Number of pages30
JournalJournal of Econometrics
Volume221
Issue number1
DOIs
Publication statusPublished - Mar 2021

JEL classifications

  • c32 - "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
  • c52 - Model Evaluation, Validation, and Selection

Keywords

  • Cointegration
  • High-dimensional data
  • Penalized regression
  • SPECS
  • Single-equation error-correction model

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