THE CHOICE BETWEEN SETS OF REGRESSORS
研究了标准线性回归模型中检验非序贯和嵌套序贯约束集时临界值的选择，发现适度提高临界值即可维持正确检验水平，并利用贝叶斯决策理论推导了两种检验算法的一致性和渐近局部功效性质。
This paper examines the choice of critical values for testing both non-sequential and nested sequential sets of constraints in the standard linear regression model. Modest increases in (e.g.) t-ratio critical values relative to their one-off values are often sufficient to maintain proper size. A Bayesian decision-theoretic approach, highlighted by the Schwarz (1978) criterion, provides a framework for deriving consistency and asymptotic local power properties of both forms of testing (data mining) algorithms.