Local Whittle estimation in time‐varying long memory series
提出一种时变局部Whittle估计量，用于估计随时间变化的长记忆参数，并证明其一致性和渐近正态性，通过蒙特卡洛模拟和实证应用展示其优于传统参数Whittle估计。
The memory parameter is usually assumed to be constant in traditional long memory time series. We relax this restriction by considering the memory a time‐varying function that depends on a finite number of parameters. A time‐varying Local Whittle estimator of these parameters, and hence of the memory function, is proposed. Its consistency and asymptotic normality are shown for locally stationary and locally non‐stationary long memory processes, where the spectral behaviour is restricted only at frequencies close to the origin. Its good finite sample performance is shown in a Monte Carlo exercise and in two empirical applications, highlighting its benefits over the fully parametric Whittle estimator proposed by Palma and Olea (2010). Standard inference techniques for the constancy of the memory are also proposed based on this estimator.