Autoregressive latent trajectory (ALT) models a synthesis of two traditions
Autoregressive latent trajectory (ALT) models a synthesis of two traditions
复制标题
DOI：
10.1177/0049124103260222
复制
发表时间：
2004-02-01
影响因子：
6.3
通讯作者：
Curran, PJ
中科院分区：
文献类型：
--
作者：
Bollen, KA;Curran, PJ
尽管有多种统计方法可用于分析纵向面板数据，但有两种方法具有特别的历史重要性：自回归（单纯形）模型和潜在轨迹（曲线）模型。这两种方法被描述为相互竞争的方法，其中一种方法优于另一种方法。我们认为自回归和轨迹模型采用了一种更具包容性的模型的特殊情况，我们称之为自回归潜在轨迹（ALT）模型。在本文中，我们详细介绍了该模型的基础统计理论和数学识别，并使用两个经验数据集演示了 ALT 模型。第一个重新分析了之前用来反对自回归模型的模拟重复测量数据集，并且我们说明了 ALT 模型如何恢复真实的潜在曲线模型。其次，我们将 ALT 模型应用于 N=3912 名成年人七年期间的实际家庭收入数据，并找到自回归和潜在轨迹过程的证据。讨论了扩展和限制。
Although there are a variety of statistical methods available for the analysis of longitudinal panel data, two approaches are of particular historical importance: the autoregressive (simplex) model and the latent trajectory (curve) model. These two approaches have been portrayed as competing methodologies such that one approach is superior to the other. We argue that the autoregressive and trajectory models ate special cases of a more encompassing model that we call the autoregressive latent trajectory (ALT) model. In this paper we detail the underlying statistical theory and mathematical identification of this model, and demonstrate the ALT model using two empirical data sets. The first reanalyzes a simulated repeated measures data set that was previously used to argue against the autoregressive model, and we illustrate how the ALT model can recover the true latent curve model. Second, we apply the ALT model to real family income data on N=3912 adults over a seven year period and find evidence for both autoregressive and latent trajectory processes. Extensions and limitations are discussed.