In this paper, we construct a new class of kernel by exponentiating conventional kernels and use them in the long run variance estimation with and without smoothing. Depending on whether the exponent is allowed to grow with the sample size, we establish different asymptotic approximations to the...

Sharp origin kernels, constructed by taking powers of the Bartlett kernel, are suggested for use in heteroskedasticity and autocorrelation consistent (HAC) estimation with no truncation (or bandwidth) parameter. When the power parameter (rho) is fixed, analysis and simulations indicate that...