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异方差与自相关稳健推断中的检验最优核选择AI 解读

The paper investigates the optimal kernel choice in heteroskedasticity and autocorrelation robust tests based on the fixed-b asymptotics. In parallel with the optimality of the quadratic spectral kernel under the asymptotic mean squared error criterion of the point estimator of the long run variance as considered in Andrews (1991) , we show that the optimality of the quadratic spectral kernel continues to hold under the testing-oriented criterion of Sun, Philips and Jin…

时间序列核平滑预渐近推断的固定带宽视角AI 解读

This paper develops robust testing procedures for nonparametric kernel methods in the presence of temporal dependence of unknown forms. Based on the fixed-bandwidth asymptotic variance and the pre-asymptotic variance, we propose a heteroskedasticity and autocorrelation robust (HAR) variance estimator that achieves double robustness — it is asymptotically valid regardless of whether the temporal dependence is present or not, and whether the kernel smoothing bandwidth is held…

斜率变化的断点估计：水平模型与一阶差分模型AI 解读

This paper analyses two break point estimators: one for a univariate slope‐shift model under unit root errors, the other for its first difference (a mean shift model). The asymptotic theory is developed for the estimators under a specific Pitman drift, assuming the break magnitude is within a T−1/2 neighbourhood of zero. Compared to the existing asymptotics assuming a fixed break magnitude or a shrinking one converging at a slower rate than T1/2, the limiting distributions…