Estimation of long-memory parameter in stationary and non-stationary curve time series
Date: 28 May 2020, Thursday
Time: 10 am
Speaker: A/Prof. Hanlin Shang (ANU)
Abstract: We study a functional version of fractionally integrated stationary and nonstationary time series, covering the functional unit root as a special case. The functional time series are projected onto a finite number of sub-spaces, the level of stationarity/non-stationary allowed to vary over them. Through the classic functional principal component analysis of the sample variance operator, we obtain the eigenvalues and eigenfunctions which span a sample version of the dominant subspace. Furthermore, we introduce a simple ratio criterion to consistently estimate the dimension of the dominant sub-space, and use a memory parameter estimator, such as local Whittle estimator, to estimate the memory parameter. Monte-Carlo simulation studies and empirical applications are given to examine the finite-sample performance of the developed techniques.
Link: https://anu.zoom.us/j/425258947
Date: 28 May 2020, Thursday
Time: 10 am
Speaker: A/Prof. Hanlin Shang (ANU)
Abstract: We study a functional version of fractionally integrated stationary and nonstationary time series, covering the functional unit root as a special case. The functional time series are projected onto a finite number of sub-spaces, the level of stationarity/non-stationary allowed to vary over them. Through the classic functional principal component analysis of the sample variance operator, we obtain the eigenvalues and eigenfunctions which span a sample version of the dominant subspace. Furthermore, we introduce a simple ratio criterion to consistently estimate the dimension of the dominant sub-space, and use a memory parameter estimator, such as local Whittle estimator, to estimate the memory parameter. Monte-Carlo simulation studies and empirical applications are given to examine the finite-sample performance of the developed techniques.
Link: https://anu.zoom.us/j/425258947