Coruscation

This paper explores prediction in time series in which the data is
generated by a curve-valued autoregression process. It develops a
novel technique, the predictive factor decomposition, for estimation
of the autoregression operator, which is designed to be better suited
for prediction purposes than the principal components method.

The technique is based on finding a reduced-rank approximation to the
autoregression operator that minimizes the norm of the expected
prediction error. The new method is illustrated by an analysis of the
dynamics of Eurodollar futures rates term structure. We restrict the
sample to the period of normal growth and find that in this subsample
the predictive factor technique not only outperforms the principal
components method but also performs on par with the best available
prediction methods.

Curve Forecasting by Functional Autoregression
Presenter(s) Alexei Onatski, Columbia University
Co-Author(s) Vladislav Kargin, Cornerstone Research
Session Chair James Stock, Harvard University