Historical demography is in transition from a data-poor to a data-rich environment. Previous generations relied on demographic models to squeeze as much information as possible from the small amounts of data available. In this new era of large data sets, researchers are creating both regional and international historical data sets of unprecedented size and depth. When examined closely, however, the regression methods used today make the same simplifying assumptions that generated the key advances of earlier generations. These new methods require demographic insight to inform analyses and enrich conclusions.

中文翻译：

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历史人口学模型的演变

历史人口学正在从数据匮乏的环境转变为数据丰富的环境。前几代人依靠人口统计模型从少量可用数据中提取尽可能多的信息。在这个大数据集的新时代，研究人员正在创建规模和深度前所未有的区域和国际历史数据集。然而，当仔细研究时，今天使用的回归方法做出了与前几代人的关键进步相同的简化假设。这些新方法需要人口统计洞察力来为分析提供信息并丰富结论。