Publication Type
Working Paper
Version
publishedVersion
Publication Date
6-2022
Abstract
This paper explores weak identification issues arising in commonly used models of economic and financial time series. Two highly popular configurations are shown to be asymptotically observationally equivalent: one with long memory and weak autoregressive dynamics, the other with antipersistent shocks and a near-unit autoregressive root. We develop a data-driven semiparametric and identification-robust approach to inference that reveals such ambiguities and documents the prevalence of weak identification in many realized volatility and trading volume series. The identification-robust empirical evidence generally favors long memory dynamics in volatility and volume, a conclusion that is corroborated using social-media news flow data.
Keywords
Realized volatility, Weak identification, Disjoint confidence sets, Trading volume, Long memory
Discipline
Econometrics | Economic Theory
Research Areas
Econometrics
First Page
1
Last Page
49
Publisher
SMU Economics and Statistics Working Paper Series Paper No. 08-2022
City or Country
Singapore
Citation
LI, Jia; PHILLIPS, Peter C. B.; SHI, Shuping; and Jun YU.
Weak identification of long memory with implications for inference. (2022). 1-49.
Available at: https://ink.library.smu.edu.sg/soe_research/2616
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.