Fixed-b asymptotics for blockwise empirical likelihood

We describe an extension of the fixed-b approach introduced by Kiefer and Vogelsang (2005) to the empirical likelihood estimation framework. Under fixed-b asymptotics, the empirical likelihood ratio statistic evaluated at the true parameter converges to a nonstandard yet pivotal limiting distribution that can be approximated numerically. The impact of the bandwidth parameter and kernel choice is reected in the fixed-b limiting distribution. Compared to the χ²-based inference procedure used by Kitamura (1997) and Smith (2011), the fixed-b approach provides a better approximation to the finite sample distribution of the empirical likelihood ratio statistic. Correspondingly, as shown in our simulation studies, the confidence region based on the fixed-b approach has more accurate coverage than its traditional counterpart.