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Forecasting using sparse cointegration
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DOI：10.1016/j.ijforecast.2016.04.005.png)
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Abstract
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This paper proposes a sparse cointegration method. Cointegration analysis is used to estimate the long-run equilibrium relationships between several time series, with the coefficients of these long-run equilibrium relationships being the cointegrating vectors. We provide a sparse estimator of the cointegrating vectors, where sparse estimation means that some elements of the cointegrating vectors are estimated to be exactly zero. The sparse estimator is applicable in high-dimensional settings, where the time series is short compared to the number of time series. Our method achieves better estimation and forecast 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 forecast accuracy gains relative to the Johansen method. (C) 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Lasso
Reduced rank regression
Sparse estimation
Time series forecasting
Vector error correction model
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