Block Constraints in Age-Period-Cohort Models with Unequal-width Intervals
Block Constraints in Age-Period-Cohort Models with Unequal-width Intervals
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DOI：
10.1177/0049124115585359
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发表时间：
2016-11-01
影响因子：
6.3
通讯作者：
Hodges, James S.
中科院分区：
文献类型：
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作者：
Luo, Liying;Hodges, James S.
半周期队列（APC）模型的目的是估计年龄，时间段和队列成员的独立影响。然而，APC模型存在一个识别问题：由于年龄、时期和队列之间存在精确的线性相关性，因此没有最适合数据的独立效应的唯一估计。在为解决这个问题而提出的方法中，使用年龄、时期和队列类别的不等间隔宽度似乎打破了精确的线性依赖关系，从而解决了识别问题。然而，本文表明，识别问题仍然存在于这些模型中;事实上，它们只是隐含地对年龄，时期和队列效应施加多个块约束以实现可识别性。这些约束取决于年龄、时期和队列间隔的宽度的任意选择，并且可能对估计值产生重要影响。由于这些假设在实证研究中很难（如果不是不可能的话）验证，因此它们在性质上与其他约束估计量的假设没有什么不同。因此，如果没有明确的理由来证明其限制的合理性，就不应使用不等间隔方法。
Age-period-cohort (APC) models are designed to estimate the independent effects of age, time periods, and cohort membership. However, APC models suffer from an identification problem: There are no unique estimates of the independent effects that fit the data best because of the exact linear dependency among age, period, and cohort. Among methods proposed to address this problem, using unequal-interval widths for age, period, and cohort categories appears to break the exact linear dependency and thus solve the identification problem. However, this article shows that the identification problem remains in these models; in fact, they just implicitly impose multiple block constraints on the age, period, and cohort effects to achieve identifiability. These constraints depend on an arbitrary choice of widths for the age, period, and cohort intervals and can have nontrivial effects on the estimates. Because these assumptions are extremely difficult, if not impossible, to verify in empirical research, they are qualitatively no different from the assumptions of other constrained estimators. Therefore, the unequal-intervals approach should not be used without an explicit rationale justifying their constraints.