局部修整最小二乘：可能非平稳模型中的常规推断
Locally trimmed least squares: conventional inference in possibly nonstationary models
摘要：
一种新的IV估计方法，我们长期本地修剪LS（LTLS），产生估计（混合）高斯极限分布的情况下，数据可能是弱或强持久的。特别是，我们允许非线性预测类型的回归，其中回归变量可以是固定的短/长记忆以及非固定的长记忆过程或几乎集成的阵列。所得t检验具有常规极限分布（即N（0; 1）），不含（接近单位和长记忆）干扰参数。在回归量是分数过程的情况下，不需要记忆参数的初步估计。因此，从业者可以进行推理，同时对数据中的确切依赖结构不可知。通过利用时间趋势变量的适当核函数，对OLS工具应用一定的时序修整，获得LTLS估计量。借助仿真实验研究了基于LTLS的t检验的有限样本性能。还提供了股票收益率的可预测性的实证应用。
英文摘要：
A novel IV estimation method, that we term Locally Trimmed LS (LTLS), is developed which yields estimators with (mixed) Gaussian limit distributions in situations where the data may be weakly or strongly persistent. In particular, we allow for nonlinear predictive type of regressions where the regressor can be stationary short/long memory as well as nonstationary long memory process or a nearly integrated array. The resultant t-tests have conventional limit distributions (i.e. N(0; 1)) free of (near to unity and long memory) nuisance parameters. In the case where the regressor is a fractional process, no preliminary estimator for the memory parameter is required. Therefore, the practitioner can conduct inference while being agnostic about the exact dependence structure in the data. The LTLS estimator is obtained by applying certain chronological trimming to the OLS instrument via the utilisation of appropriate kernel functions of time trend variables. The finite sample performance of LTLS based t-tests is investigated with the aid of a simulation experiment. An empirical application to the predictability of stock returns is also provided.
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