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Full-Text Articles in Social and Behavioral Sciences
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite …
Weak Identification Of Long Memory With Implications For Inference, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Weak Identification Of Long Memory With Implications For Inference, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
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.