Title: Forecasting using sparse cointegration
Authors: Wilms, Ines ×
Croux, Christophe #
Issue Date: 2016
Conference: The Joint Statistical Meetings (JSM) location:Chicago (US) date:30 July – 4 August 2016
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. We provide a sparse estimator of the cointegrating vectors. Sparsity means that some elements of the cointegrating vectors are estimated as exactly zero, improving interpretability. The sparse estimator is applicable in high-dimensional settings, where the time series length is short relative to the number of time series. Our method achieves better estimation accuracy than the traditional Johansen method in sparse and/or high-dimensional settings. We use the sparse method for interest rate growth forecasting and consumption growth forecasting. The sparse cointegration method leads to important gains in forecast accuracy compared to the Johansen method.
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

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