Weak Identification of Long Memory with Implications for Inference
Shuping Shi and
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Jia Li: Singapore Management University
No 2334, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
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 (search for similar items in EconPapers)
JEL-codes: C12 C13 C58 (search for similar items in EconPapers)
Pages: 41 pages
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Working Paper: Weak Identification of Long Memory with Implications for Inference (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cwl:cwldpp:2334
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