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Title: Estimation pitfalls when the noise is not i.i.d. Authors:  Liudas Giraitis - Queen Mary University of London (United Kingdom) [presenting]
Masanobu Taniguchi - Waseda University (Japan)
Murad Taqqu - Boston University (United States)
Abstract: Whittle estimation is extended to linear processes with a general stationary ergodic martingale difference noise. We show that such an estimation is valid for standard parametric time series models with smooth bounded spectral densities, e.g. ARMA models. Furthermore, we clarify the impact of the hidden dependence in the noise on such estimation. We show that although the asymptotic normality of the Whittle estimates may still hold, the presence of dependence in the noise impacts the limit variance. Hence, the standard errors and confidence intervals valid under i.i.d. noise may not be applicable and thus require correction. The goal is to raise awareness to the impact of a non i.i.d. noise in applied work.