Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators
给出Bootstrap方法为GMM估计的t检验和过度识别检验提供渐近改进临界值的条件，尤其针对相依数据需采用块抽样，蒙特卡洛实验表明Bootstrap能减少使用一阶渐近理论临界值时的水平误差。
Monte Carlo experiments have shown that tests based on generalized-method-ofmoments estimators often have true levels that differ greatly from their nominal levels when asymptotic critical values are used. This paper gives conditions under which the bootstrap provides asymptotic refinements to the critical values of t tests and the test of overidentifying restrictions. Particular attention is given to the case of dependent data. It is shown that with such data, the bootstrap must sample blocks of data and that the formulae for the bootstrap versions of test statistics differ from the formulae that apply with the original data. The results of Monte Carlo experiments on the numerical performance of the bootstrap show that it usually reduces the errors in level that occur when critical values based on first-order asymptotic theory are used. The bootstrap also provides an indication of the accuracy of critical values obtained from first-order asymptotic theory.