Unifying Estimation and Inference for Linear Regression with Stationary and Integrated or Near-Integrated Variables
针对平稳变量和整合变量最小二乘估计极限分布不同的问题，提出基于加权估计的统一推断方法，并采用随机加权自助法构建置信区域，模拟显示优于现有方法，还用于研究资产收益的可预测性。
Abstract There is a discrepancy in the limiting distributions of least-squares estimators for stationary and integrated variables. For statistical inference, it must be decided which distribution should be used in advance. This motivates us to develop a unifying inference procedure based on weighted estimation. The asymptotic distributions of the proposed estimators are developed and a random weighting bootstrap method is proposed for constructing confidence regions. The proposed method outperforms existing methods (with time constant or time-varying error variance) in simulations. We further study the predictability of asset returns in a setting where some of our state variables are endogenous.