Publication Type
Journal Article
Version
submittedVersion
Publication Date
6-2015
Abstract
Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order. These methods enable simultaneous order estimation of the cointegrating rank and autoregressive order in conjunction with oracle-like efficient estimation of the cointegrating matrix and transient dynamics. As such they offer considerable advantages to the practitioner as an automated approach to the estimation of cointegrated systems. The paper develops the new methods, derives their limit theory, discusses implementation, reports simulations, and presents an empirical illustration with macroeconomic aggregates.
Keywords
Adaptive shrinkage, Automation, Cointegrating rank, Lasso regression, Oracle efficiency, Transient dynamics, Vector error correction
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Theory
Volume
31
Issue
3
First Page
581
Last Page
646
ISSN
0266-4666
Identifier
10.1017/S026646661500002X
Publisher
Cambridge University Press
Citation
LIAO, Zhipeng and Peter C. B. PHILLIPS.
Automated estimation of vector error correction models. (2015). Econometric Theory. 31, (3), 581-646.
Available at: https://ink.library.smu.edu.sg/soe_research/1872
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/10.1017/S026646661500002X