Inference for Time-varying Factor Models under Local Stationarity
Holder： Weichi Wu（Tsinghua University）
Time：2025-05-22 15:10-17:00
Location：Room 217, Guanghua Building 2
Abstract:
This paper considers estimation of and testing for a class of locally stationary time series factor models with evolutionary dynamics, where the entries and dimension of the factor loading matrix are allowed to vary with time while the factors and idiosyncratic noise components are locally stationary. We propose an adaptive sieve estimator for the span of the time-varying loading matrix of a locally stationary factor process. A uniformly consistent estimator of the effective number of factors is developed via eigenanalysis of a non-negative definite time-varying matrix. We also propose a possibly high-dimensional bootstrap test for the hypothesis of constant factor loadings by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. This test avoids the assumption that factors and idiosyncratic errors are stationary or the covariance matrix of factors is time-invariant. Our results cover both the case of white noise idiosyncratic errors and the case of serially correlated idiosyncratic errors. We examine the finite sample performance of our proposed estimator and test via simulation studies and real data analysis.
About the Speaker:
吴未迟，清华大学统计与数据科学系副教授。主要研究方向为复杂时间序列，非参数统计，统计网络，变点问题。担任Statistics and Probability Letters 的Associate Editor。
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