Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models
扩展了扰动长记忆序列的对数周期图回归，允许信号与噪声相关，证明了估计量的相合性和渐近正态性，并提出了检验信号与噪声是否相关的局部检验方法。
This paper proposes an extension of the log periodogram regression in perturbed long memory series that accounts for the added noise, also allowing for correlation between signal and noise, which represents a common situation in many economic and financial series. Consistency (for d < 1) and asymptotic normality (for d < 3/4) are shown with the same bandwidth restriction as required for the original log periodogram regression in a fully observable series, with the corresponding gain in asymptotic efficiency and faster convergence over competitors. Local Wald, Lagrange Multiplier and Hausman type tests of the hypothesis of no correlation between the latent signal and noise are also proposed.