Publication Type
Journal Article
Version
submittedVersion
Publication Date
2-2023
Abstract
Limit distribution theory in the econometric literature for functional coefficient cointegrating regression is incorrect in important ways, influencing rates of convergence, distributional properties, and practical work. The correct limit theory reveals that components from both bias and variance terms contribute to variability in the asymptotics. The errors in the literature arise because random variability in the bias term has been neglected in earlier research. In stationary regression this random variability is of smaller order and can be ignored in asymptotic analysis but not without consequences for finite sample performance. Implications of the findings for rate efficient estimation are discussed. Simulations in the Online Supplement provide further evidence supporting the new limit theory in nonstationary functional coefficient regressions.
Keywords
Bandwidth selection, Bias variability, Functional coefficient cointegration, Kernel regression, Nonstationarity
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
232
Issue
2
First Page
469
Last Page
489
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2021.09.007
Publisher
Elsevier
Citation
PHILLIPS, Peter C. B. and WANG, Ying.
When bias contributes to variance: True limit theory in functional coefficient cointegrating regression. (2023). Journal of Econometrics. 232, (2), 469-489.
Available at: https://ink.library.smu.edu.sg/soe_research/2775
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/10.1016/j.jeconom.2021.09.007