Automated Bandwidth Selection for Inference in Linear Models With Time‐Varying Coefficients
研究了时变系数线性模型中核估计平滑参数（带宽）的自动选择方法，通过交叉验证、非参数AIC和自助法进行选择，模拟表明交叉验证和依赖自助法效果最好。
ABSTRACT The problem of selecting the smoothing parameter, or bandwidth, for kernel‐based estimators of time‐varying coefficients in linear models with possibly endogenous explanatory variables is considered. We examine automated bandwidth selection by means of cross‐validation, a nonparametric variant of Akaike's information criterion, and bootstrap procedures based on wild bootstrap and dependent wild bootstrap resampling schemes. Our simulations show that data‐driven selectors based on cross‐validation and the dependent wild bootstrap are the most successful overall in a variety of settings that are relevant in econometrics. Empirical examples illustrate the practical use of the automated procedures.