Second order expansions of estimators in nonparametric moment conditions models with weakly dependent data

This paper considers estimation of nonparametric moment conditions models with weakly dependent data. The estimator is based on a local linear version the generalized empirical likelihood approach, and an alternative to popular method estimator. derives uniform convergence rates pointwise asymptotic normality resulting also develops second order stochastic expansions (under standard undersmoothing condition) that explain better finite sample performance compared efficient moments estimator, can be used obtain (second order) bias corrected estimators. Monte Carlo simulations application illustrate competitive properties usefulness proposed estimators corrections.