Births Time Series Models and Structural Interpretations
出生人数时间序列模型及其结构解释AI 解读 ↓
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出生人数时间序列模型及其结构解释AI 解读 ↓
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Saboia (1977) has developed a theory relating a particular type of births autoregressive moving average (ARMA) model to the classical deterministic cohort model of population growth. Reexamination of the data used by Saboia indicates that his estimated births models contain common factors and insignificant coefficients and cannot be regarded as evidence in favor of his argument. It is shown, however, that well-known theory on the relationship between deterministic and stochastic structural models and ARMA processes does provide the link and that Saboia's argument can be regarded as a special case of this general theory.
矩阵值 CP 因子模型的识别问题与准极大似然估计
Matrix-valued time series, arising in diverse fields such as economics, neuroscience, and recommender systems, have become increasingly prominent in modern data analysis. Among various modeling frameworks, the matrix-variate CP-factor model represents an important and widely applicable class for capturing low-rank structures in matrix time series. In this paper, we provide a unified treatment for the identification problem of CP factor models using both analytic and…
高维近似因子时间序列模型的主成分分析:假设、渐近理论与识别
We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results establishing the necessary and sufficient conditions for consistency and asymptotic normality of the estimators, which hold when both the cross-sectional dimension $n$ and the sample size $T$ tend to infinity. Special emphasis is placed on identification. First, we show that the common and…
异方差噪声下高维时间序列的因子模型估计
Factor modeling provides a framework for extracting common components from high-dimensional time series. We develop an estimator of the factor loading space that combines contemporaneous covariance with lagged autocovariance information. Under cross-sectionally heteroskedastic white noise, the contemporaneous covariance contains heterogeneous diagonal noise contributions. We apply the existing HeteroPCA algorithm to the combined estimation matrix to correct this…
张量因子模型中多个变点的检测与模态识别
We study the problem of modeling high-dimensional tensor-valued time series under a Tucker decomposition-based factor model while permitting the presence of multiple structural change points. Such data call for a method that (i) handles high dimensionality and complex dependence while achieving consistency in multiple change points and further, (ii) addresses a unique challenge posed by the multi-dimensional array setting, of identifying the modes associated with each change…
函数型时间序列的近似因子模型
We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information…
高维张量时间序列的 CP 因子分解与双重投影迭代
Abstract We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed based on the serial dependence structure of the data. The asymptotic properties of the proposed estimator are established under a general setting as long as the factor loading vectors…