SMS scnews item created by John Ormerod at Wed 21 Jun 2017 1030
Type: Seminar
Distribution: World
Expiry: 30 Jun 2017
Calendar1: 30 Jun 2017 1400-1500
CalLoc1: AGR Carslaw 829
CalTitle1: Sparse cointegration
Auth: jormerod@pjormerod5.pc (assumed)

# Statistics Seminar: Ines Wilms -- Sparse cointegration


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. 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. We show that forecast performance can be improved by
sparsely estimating the cointegrating vectors.

Joint work with Christophe Croux.


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