Translating Prior Information Across Specifications to Improve Predictive Accuracy
研究如何将适用于简单线性模型的经济学先验信息转化为非线性模型使用，发现这样做能提升样本外预测精度，相当于扩充了样本量，对房地产估价等领域的预测建模有参考价值。
Unrestricted nonlinear models typically outperform their simple linear counterparts in the hedonic pricing and mass assessment fields. Economic theory, however, suggests prior information that most naturally applies to the simple linear model. This article examines the consequences of translating this prior information across specifications. The results show that the addition of the prior information improved the ex-sample prediction accuracy over all sample sizes examined. The prior information effectively augments the sample size, thus extending the domain of these models.