Innocents in the Forest: Forecasting and Research Methods
提出预测指南并评估研究方法，指出惯性导致高度自相关的时间序列，使简单线性外推在短期预测中优于复杂方法，并驳斥了关于分类、回归和模型拟合的三个常见误区。
This article presents guidelines for making forecasts and draws inferences about research techniques. Inertia produces highly autocorrelated time series in which random events have lasting effects. Such series make it easy to draw incorrect inferences about causal processes. They also make it easy to predict accurately over the short run, using variants of linear extrapolation. In forecasting, simplicity usually works better than complexity. Complex forecasting methods mistake random noise for information. Moderate expertise proves as effective as great expertise. Linear functions make better judgments than people. Analogous principles probably apply to research. Three common myths do not stand up to scrutiny: One, using fewer categories does not reduce the effects of observational errors. Two, least-squares regression does not produce reliable findings. Three, better fitting models do not predict better, even in the very short run, if researchers use squared errors to measure fits to historical data and forecasting accuracies. However, better fitting models would predict better if researchers would replace squared-error criteria with more reliable measures.