An Alternative Asymptotic Analysis of Residual-Based Statistics
提出一种推导残差统计量极限分布的新方法，不要求统计量对模型参数具有渐近光滑性，适用于离散选择模型和动态波动率模型的设定检验。
This paper presents an alternative method to derive the limiting distribution of residual-based statistics. Our method does not impose an explicit assumption of (asymptotic) smoothness of the statistic of interest with respect to the model's parameters and thus is especially useful in cases where such smoothness is difficult to establish. Instead, we use a locally uniform convergence in distribution condition, which is automatically satisfied by residual-based specification test statistics. To illustrate, we derive the limiting distribution of a new functional form specification test for discrete choice models, as well as a runs-based tests for conditional symmetry in dynamic volatility models. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.