论文标题
本地修剪最小二乘：可能非组织模型的常规推断
Locally trimmed least squares: conventional inference in possibly nonstationary models
论文作者
论文摘要
我们开发了一种新的IV估计方法，即我们在局部修剪LS（LTL）的术语，该方法在数据可能弱或强持续存在的情况下产生具有（混合）高斯极限分布的估计器。特别是，我们允许回归的非线性预测类型的回归类型，其中回归器可以是固定的短/长存储器以及非平稳的长期内存过程或几乎集成的数组。所得的t检验具有传统的极限分布（即n（0; 1）），该分布没有（接近统一和长度记忆）滋扰参数。如果回归器是一个分数过程，则不需要记忆参数的初步估计器。因此，从业者可以进行推断，同时对数据中的确切依赖性结构不可知。 LTLS估计量是通过利用适当的时间趋势变量的适当内核函数应用于OLS仪器来获得的。借助仿真实验研究了基于LTLS的t检验的有限样本性能。还提供了股票回报可预测性的经验应用。
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.
