When the exponent is fixed, the new estimator is inconsistent and has a nonstandard limiting distribution. When the exponent is passed to infinity with the sample size, the new estimator is consistent and shown to be asymptotically normal.
Depending on whether the exponent parameter is allowed to grow with the sample size, we establish different asymptotic approximations to the sampling distribution of the proposed estimators. Sun, Yixiao Jin, SainanĪ new class of kernels for long‐run variance and spectral density estimation is developed by exponentiating traditional quadratic kernels. SPECTRAL DENSITY ESTIMATION AND ROBUST HYPOTHESIS TESTING USING STEEP ORIGIN KERNELS WITHOUT TRUNCATION * SPECTRAL DENSITY ESTIMATION AND ROBUST HYPOTHESIS TESTING USING STEEP ORIGIN KERNELS WITHOUT.
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