Long Run Variance Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation

• Main Authors: PHILLIPS, Peter C. B., SUN, Yixiao, JIN, Sainan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Heteroscedasticity and autocorrelation consistent standard error; Data-determined kernel estimation; Long run variance; Power parameter; Sharp origin kernel; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/318; https://ink.library.smu.edu.sg/context/soe_research/article/1317/viewcontent/LongRunVarianceEstimation_sharpOriginKernels_2007.pdf
• Institution: Singapore Management University

A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or bandwidth) parameter. As the power parameter ([rho]) increases, the...