BOOTSTRAP CRITICAL-VALUES FOR TESTS BASED ON GENERALIZED-METHOD-OF-MOMENTS ESTIMATORS

Monte Carlo experiments have shown that tests based on generalized-met hod-of-moments estimators often have true levels that differ greatly f rom their nominal levels when asymptotic critical values are used. Thi s paper gives conditions under which the bootstrap provides asymptotic refinements to the critical values of t tests and the test of overide ntifying restrictions. Particular attention is given to the case of de pendent data. It is shown that with such data, the bootstrap must samp le 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 performa nce 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 o f critical values obtained from first-order asymptotic theory.