对非平稳性稳健的预白化长期方差估计
Prewhitened Long-Run Variance Estimation Robust to Nonstationarity
AI总结：
本文提出一种对非平稳性稳健的非参数非线性VAR预白化长期方差估计量，用于构造自相关和异方差稳健标准误，并建立更精确的MSE界以确定数据依赖带宽，从而改善假设检验的原假设拒绝率和功效。
摘要：
我们提出一种非参数非线性VAR预白化长期方差（LRV）估计量，用于构造对自相关和异方差稳健的标准误，可用于包括线性回归模型在内的多种情境下的假设检验。现有方法要么仅在平稳性条件下具有理论有效性，并且在非平稳性下有限样本性质较差（即固定-b方法），要么在原假设下具有理论有效性，但在非平稳备择假设下导致检验不一致（即固定-b和传统HAC估计量均如此）。与已知不可靠的以往预白化程序不同，所提出的估计量明确考虑了非平稳性，并能够得到具有准确原假设拒绝率和良好单调功效的检验。我们还建立了比以往更精确的LRV估计均方误差（MSE）界，并利用这些界来确定数据依赖的带宽。
英文摘要：
We introduce a nonparametric nonlinear VAR prewhitened long-run variance (LRV) estimator for the construction of standard errors robust to autocorrelation and heteroskedasticity that can be used for hypothesis testing in a variety of contexts including the linear regression model. Existing methods either are theoretically valid only under stationarity and have poor finite-sample properties under nonstationarity (i.e., fixed-b methods), or are theoretically valid under the null hypothesis but lead to tests that are not consistent under nonstationary alternative hypothesis (i.e., both fixed-b and traditional HAC estimators). The proposed estimator accounts explicitly for nonstationarity, unlike previous prewhitened procedures which are known to be unreliable, and leads to tests with accurate null rejection rates and good monotonic power. We also establish MSE bounds for LRV estimation that are sharper than previously established and use them to determine the data-dependent bandwidths.
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