论文标题
非平稳因子模型的自适应估计和静态因子负载的测试
Adaptive Estimation for Non-stationary Factor Models And A Test for Static Factor Loadings
论文作者
论文摘要
本文考虑了具有进化时间动力学的一类局部固定时间序列因子模型的估计和测试。特别是，允许因子加载矩阵的条目和尺寸随时间而变化，而因子和特质噪声组件是局部固定的。我们为不同的加载矩阵和局部固定因子过程的跨度提出了一个自适应筛估计器。通过对非负定时间变化矩阵的特征分析研究了对有效因子数量的均匀估计量。通过将整个时间序列的协方差矩阵与其局部对应物进行比较，提出了一个可能具有静态因子负荷假设假设假设的假设假设的静态因子负荷假设的测试。我们通过模拟研究和实际数据分析检查我们的估计器和测试。最后，我们所有的结果都在以下流行但不同的假设上：（a）固定尺寸或不同尺寸的白噪声特质误差，以及（b）相关的特质误差与尺寸不同。
This paper considers the estimation and testing of a class of locally stationary time series factor models with evolutionary temporal dynamics. In particular, the entries and the dimension of the factor loading matrix are allowed to vary with time while the factors and the idiosyncratic noise components are locally stationary. We propose an adaptive sieve estimator for the span of the varying loading matrix and the locally stationary factor processes. A uniformly consistent estimator of the effective number of factors is investigated via eigenanalysis of a non-negative definite time-varying matrix. A possibly high-dimensional bootstrap-assisted test for the hypothesis of static factor loadings is proposed by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. We examine our estimator and test via simulation studies and real data analysis. Finally, all our results hold at the following popular but distinct assumptions: (a) the white noise idiosyncratic errors with either fixed or diverging dimension, and (b) the correlated idiosyncratic errors with diverging dimension.
