Inference-Without-Smoothing in the Presence of Nonparametric Autocorrelation
研究在非参数自相关存在时，如何在不使用平滑技术的情况下一致估计谱密度在零频率处的值，并用于线性联立方程模型中结构参数的渐近正态估计的学生化，蒙特卡洛模拟验证了有限样本表现。
correlation, and smoothing is used in the estimation of f(O). We give conditions under which f(O) can be consistently estimated without smoothing. The conditions are relevant to inference on slope parameters in models with an intercept and strictly exogenous regressors, and allow regressors and disturbances to collectively have considerable stationary long memory and to satisfy only mild, in some cases minimal, moment conditions. The estimate of f(O) dominates smoothed ones in the sense that it can have mean squared error of order n -1, where n is sample size. Under standard additional regularity conditions, we extend the estimate of f(O) to studentize asymptotically normal estimates of structural parameters in linear simultaneous equations systems. A small Monte Carlo study of finite sample behavior is included.