Sparse cointegration

TL;DR

This paper introduces a sparse estimation method for cointegrating vectors in multivariate time series, improving accuracy especially in high-dimensional or small-sample scenarios, and demonstrates its effectiveness through simulations and empirical applications.

Contribution

It proposes a novel sparse estimator for cointegrating vectors combining penalized VAR and reduced rank regression, enhancing estimation in high-dimensional and low-sample contexts.

Findings

Sparse estimator outperforms Johansen in accuracy

Improves forecast performance in high-dimensional systems

Effective in empirical tests on interest rates

Abstract

Cointegration analysis is used to estimate the long-run equilibrium relations between several time series. The coefficients of these long-run equilibrium relations are the cointegrating vectors. In this paper, we provide a sparse estimator of the cointegrating vectors. The estimation technique is sparse in the sense that some elements of the cointegrating vectors will be estimated as zero. For this purpose, we combine a penalized estimation procedure for vector autoregressive models with sparse reduced rank regression. The sparse cointegration procedure achieves a higher estimation accuracy than the traditional Johansen cointegration approach in settings where the true cointegrating vectors have a sparse structure, and/or when the sample size is low compared to the number of time series. We also discuss a criterion to determine the cointegration rank and we illustrate its good performance in several simulation settings. In a first empirical application we investigate whether the expectations hypothesis of the term structure of interest rates, implying sparse cointegrating vectors, holds in practice. In a second empirical application we show that forecast performance in high-dimensional systems can be improved by sparsely estimating the cointegration relations.