"This paper reviews the growing literature on population forecasting to examine a curious paradox: despite continuing refinements in the specification of models used to represent population dynamics, simple exponential growth models, it is claimed, continue to outperform such more complex models in forecasting exercises. Shrinking a large complex model in order to simplify it typically involves two processes: aggregation and decomposition. Both processes are known to introduce biases into the resulting representations of population dynamics. Thus it is difficult to accept the conclusion that simple models outperform complex models. Moreover, assessments of forecasting performance are notoriously difficult to carry out, because they inevitably depend not only on the models used but also on the particular historical periods selected for examination.... This paper reviews some of the recent debate on the simple versus complex modeling issue and links it to the questions of model bias and distributional momentum impacts." (SUMMARY IN FRE)

中文翻译：

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人口预测：简单模型是否优于复杂模型？

“这篇论文回顾了不断增长的人口预测文献，以检验一个奇怪的悖论：尽管用于表示人口动态的模型规范不断改进，但据称，简单的指数增长模型在预测练习中继续优于此类更复杂的模型。缩小大型复杂模型以简化它通常涉及两个过程：聚合和分解。众所周知，这两个过程都会在人口动态的结果表示中引入偏差。因此，很难接受简单模型优于复杂模型的结论。此外。 ，众所周知，对预测性能的评估很难进行，因为它们不可避免地不仅取决于所使用的模型，还取决于选择用于检查的特定历史时期……本文回顾了最近关于简单与复杂建模问题的一些辩论，并将其与模型偏差和分布问题联系起来。动量影响。”（免费摘要）