Biased Prediction of Housing Values
利用1983年美国住房调查数据，比较了三种Stein型经验贝叶斯估计与最小二乘估计及岭回归在异质产品价格预测中的表现，发现偏误估计在特定条件下能降低预测均方误差。
This paper introduces the use of non‐sample, prior information to the problem of predicting prices of heterogeneous products. Using data from the 1983 American Housing Survey, the predictive performance of three Stein‐like empirical Bayes estimation rules are compared to the least squares estimator and the traditional biased estimation technique, ridge regression. The biased estimators improve upon the least squares mean square error of prediction risk under certain design‐related conditions. We provide evidence of this for the housing market in this paper.