Abstract :
The two most popular bandwidth choice rules for kernel HAC estimation have been
proposed by Andrews (1991) and Newey and West (1994). This paper suggests an
alternative approach that estimates an unknown quantity in the optimal bandwidth
for the HAC estimator (called normalized curvature) using a general class of kernels,
and derives the optimal bandwidth that minimizes the asymptotic mean squared error
of the estimator of normalized curvature. It is shown that the optimal bandwidth for
the kernel-smoothed normalized curvature estimator should diverge at a slower rate
than that of the HAC estimator using the same kernel. An implementation method
of the optimal bandwidth for the HAC estimator, which is analogous to the one for
probability density estimation by Sheather and Jones (1991), is also developed. The
finite sample performance of the new bandwidth choice rule is assessed through
Monte Carlo simulations.
