Unstable Models from Incorrect Forms
通过蒙特卡洛模拟发现，看似无害的模型设定错误（如函数形式错误）会大幅增加误判结构变化的风险，导致Chow检验和自相关检验的虚假拒绝率上升。
Abstract Parametric tests for structural change are conditional on the joint hypothesis of functional form and other aspects of the model specification. This problem is often disregarded. Monte Carlo evidence using three data sets indicates that apparently innocuous specification errors can lead to substantial increases in the probability of finding structural change when it is not present in the data‐generating mechanism. Significant Chow tests and autocorrelation are much more likely when the wrong functional form is used. Maximum Chow tests falsely reject stable preferences much more often than their nominal size suggests, even when the correct model is estimated.