Semiparametric Sieve-Type Generalized Least Squares Inference
针对误差项存在依赖的线性回归模型，提出一种基于自回归近似的筛型广义最小二乘方法，并建立其渐近性质，蒙特卡洛模拟检验了有限样本表现。
This article considers the problem of statistical inference in linear regression models with dependent errors. A sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established under general conditions, including mixingale-type conditions as well as conditions which allow for long-range dependence in the stochastic regressors and/or the errors. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.