This article documents surprising learning patterns that can occur under model misspecification. An agent resorts to predictive regressions and fails to take into account autocorrelation in the dependent variable. Remarkably, when the dependent and independent variables are uncorrelated, we find cases for which the resulting out-of-sample is well above zero, which benefits the agent, in spite of the erroneous model. We refer to them as instances of unexpected opportunity. When both variables exhibit high levels of persistence, we reveal the existence of counter-intuitive configurations for which the when the absolute correlation between the series decreases. Our theoretical results are confirmed by extensive simulations and complemented by an empirical exercise of equity premium prediction for which we use 15 predictors commonly referenced in the economic literature.

中文翻译：

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错误指定的预测回归中的意外机会


本文记录了模型错误指定下可能出现的令人惊讶的学习模式。代理诉诸预测回归，但未能考虑因变量的​​自相关。值得注意的是，当因变量和自变量不相关时，我们会发现所得到的样本外远高于零的情况，这对代理有利，尽管模型是错误的。我们将它们称为意外机会的实例。当两个变量都表现出高水平的持久性时，我们揭示了反直觉配置的存在，当序列之间的绝对相关性下降时。我们的理论结果得到了广泛的模拟的证实，并通过股权溢价预测的实证练习得到了补充，我们使用了经济文献中常用的 15 个预测因子。