Title: Sparse cointegration analysis in high dimensions
Authors: Croux, Christophe
Wilms, Ines
Issue Date: 2014
Conference: LSM/CORE Seminars location:Leuven (Belgium) date:13 October 2014
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 canonical correlations analysis. The sparse cointegration procedure achieves a higher estimation accuracy than the traditional Johansen cointegration approach in simulation 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 the second empirical application we show that forecast performance in high-dimensional systems can be improved by sparsely estimating the cointegration relations.
Publication status: published
KU Leuven publication type: AMa
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven

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