MODEL BUILDING AND DATA MINING
定义了计量经济学中的数据挖掘现象，讨论了经典和贝叶斯方法在变量选择、滞后结构确定和联立方程模型设定中的优缺点及损失函数的影响。
This paper defines the phenomenon of data mining in econometrics and discusses various outcomes of and solutions to data mining. Both classical and Bayesian approaches are considered, each with notable advantages and disadvantages, and with the choice of loss function affecting critical values. Illustrative examples include variable addition and exclusion in a standard linear regression model, the choice of lag structure in a dynamic single equation, and specification in a simultaneous equations model.