Publication Type

Journal Article

Version

Preprint

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 (CUP): HSS Journals

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1017/S026646661500002X

Included in

Econometrics Commons

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