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.