Fitting Dynamic Regression Models to Seshat Data - Supplemental Material

This article presents a general statistical approach suitable for the analysis of time-resolved (time-series) cross-cultural data. The goal is to test theories about the evolutionary processes that generate cultural change. This approach allows us to investigate the effects of predictor variables (proxying for theory-suggested mechanisms), while controlling for spatial diffusion and autocorrelations due to shared cultural history (known as Galton’s Problem). It also fits autoregressive terms to account for serial correlations in the data and tests for nonlinear effects. I illustrate these ideas and methods with an analysis of processes that may influence the evolution of one component of social complexity, information systems, using the Seshat: Global History Databank.本文提出一套适配时间解析（time-resolved）跨文化数据（即时间序列跨文化数据）分析的通用统计方法，旨在检验阐释文化变迁演化进程的相关理论。该方法可用于探究预测变量（predictor variables，作为理论所提示机制的代理变量）的影响效应，同时控制由共享文化历史引发的空间扩散与自相关问题，该问题被称为高尔顿问题（Galton’s Problem）。此外，该方法还可拟合自回归项（autoregressive terms）以处理数据中的序列自相关，并能够检验非线性效应。本文以塞沙特：全球历史数据库（Seshat: Global History Databank）为数据源，通过分析可能影响社会复杂性组成部分之一——信息系统演化的进程，对上述思路与方法进行示例阐释。