Publication Type
Journal Article
Version
submittedVersion
Publication Date
3-2026
Abstract
Cointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross-section curves. Consistent cointegrating rank estimation is developed using information criteria extended to curve time series environments. The asymptotic theory involves two-parameter Gaussian processes that generalise the standard limit processes involved in cointegrating regressions. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as in standard lag order selection in autoregression, as well as in reduced rank regression with multiple time series.
Keywords
Cointegrating rank, curved cross-section data, Gaussian processes, Hilbert space, information criteria
Discipline
Econometrics
Research Areas
Econometrics
Publication
Oxford Bulletin of Economics and Statistics
ISSN
0305-9049
Identifier
10.1111/obes.70061
Publisher
Wiley
Citation
PHILLIPS, Peter C. B..
Semiparametric cointegrating rank selection for curved cross-section time series. (2026). Oxford Bulletin of Economics and Statistics.
Available at: https://ink.library.smu.edu.sg/soe_research/2871
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1111/obes.70061