Asymptotic theory and econometric practice
实证检验了经典渐近理论中模型维度固定的假设，发现工资方程文献中维度随样本量增长，并介绍了允许维度增长的新渐近理论，对计量模型构建者评估标准渐近方法有效性有指导意义。
Abstract The classical paradigm of asymptotic theory employed in econometrics presumes that model dimensionality, p , is fixed as sample size, n , tends to inifinity. Is this a plausible meta‐model of econometric model building? To investigate this question empirically, several meta‐models of cross‐ sectional wage equation models are estimated and it is concluded that in the wage‐equation literature at least that p increases with n roughly like n l/4 , while that hypothesis of fixed model dimensionality of the classical asymptotic paradigm is decisively rejected. The recent theoretical literature on ‘large‐ p ’ asymptotics is then very briefly surveyed, and it is argued that a new paradigm for asymptotic theory has already emerged which explicitly permits p to grow with n. These results offer some guidance to econometric model builders in assessing the validity of standard asymptotic confidence regions and test statistics, and may eventually yield useful correction factors to conventional test procedures when p is non‐negligible relative to n .