An automated approach towards sparse single-equation cointegration modelling

Stephan Smeekes and Etienne Wijler

Journal of Econometrics, 2021, vol. 221, issue 1, 247-276

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.

Keywords: SPECS; Penalized regression; Single-equation error-correction model; Cointegration; High-dimensional data (search for similar items in EconPapers)
JEL-codes: C32 C52 C55 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Working Paper: An Automated Approach Towards Sparse Single-Equation Cointegration Modelling (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:221:y:2021:i:1:p:247-276

DOI: 10.1016/j.jeconom.2020.07.021

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