Identifying Cointegration by Eigenanalysis

We propose a new and easy-to-use method for identifying cointegrated
components of nonstationary time series, consisting of an eigenanalysis
for a certain non-negative definite matrix. Our setting is model-free,
and we allow the integer-valued integration orders of the observable
series to be unknown, and to possibly differ. Consistency of estimates
of the cointegration space and cointegration rank is established both
when the dimension of the observable time series is fixed as sample size
increases, and when it diverges slowly. The proposed methodology is also
extended and justified in a fractional setting. A Monte Carlo study of
finite-sample performance, and a small empirical illustration, are reported.

• Speaker: Qiwei Yao — London School of Economics
• Friday 08 November 2019, 14:00–15:00
• Venue: MR12.
• Series: Statistics; organiser: Dr Sergio Bacallado.