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Activity Number: 256
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #319149 View Presentation
Title: Forecasting Using Sparse Cointegration
Author(s): Ines Wilms* and Christophe Croux
Companies: and KU Leuven
Keywords: Lasso ; Reduced rank regression ; Sparse estimation ; Time series forecasting ; Vector error correcting model

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.

Authors who are presenting talks have a * after their name.

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