Age period cohort characteristic models
Age period cohort characteristic models
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DOI：
10.1006/ssre.1999.0656
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发表时间：
2000-03-01
影响因子：
2.5
通讯作者：
O'Brien, RM
中科院分区：
文献类型：
--
作者：
O'Brien, RM
年龄阶段队列特征（APCC）模型为检验涉及年龄、时期和队列效应的理论提供了一种强有力的方法，但这种方法的大部分效力尚未得到承认。使用这种方法的研究几乎总是集中在一个单一的解释性队列特征和控制，只有年龄组和时期。即使使用这个简单的模型，我们也注意到，因变量和队列特征之间的关系不仅针对历史时期和年龄进行控制，而且还针对队列出生的时期进行控制。APCC模型可以适应“同期”变量的控制，例如年龄/特定时期的黑人百分比测量以及其他队列特征。自相关误差，由于队列残差，可以出现在APCC模型，我们推导出的方法来检测和处理这种自相关。OLS或WLS通常用于估计APCC模型中的参数;我们注意到，其他估计技术，例如，Poisson回归或Logistic回归有时可能更合适。一个实证的例子说明了这些改进和扩展使用一个非常重要的数据集。(C)北京大学出版社.
Age Period Cohort Characteristic (APCC) models provide a powerful method for testing theories that involve age, period, and cohort effects, but much of that power remains unrecognized. Studies that use this method almost always focus on a single explanatory cohort characteristic and control for only age groups and periods. Even with this simple model, we note that the relationship between the dependent variable and the cohort characteristic is controlled not only for historical period and for age, but also for the period in which the cohort was born. The APCC models can accommodate controls for "contemporaneous" variables such as age/period-specific measures of percentage Black as well as for additional cohort characteristics. Autocorrelated errors, due to cohort residuals, can arise in APCC models, and we derive methods to detect and deal with this autocorrelation. OLS or WLS typically are employed to estimate the parameters in APCC models; we note that other estimation techniques, e.g., Poisson regression or logistic regression may at times be more appropriate. An empirical example illustrates these refinements and extensions using a substantively important data set. (C) 2000 Academic Press.