Working Paper: NBER ID: w16127
Authors: Patrick M. Kline; Andres Santos
Abstract: \nWe propose a generalization of the wild bootstrap of Wu (1986) and Liu (1988) based upon perturbing the scores of M-estimators. This "score bootstrap" procedure avoids recomputing the estimator in each bootstrap iteration, making it substantially less costly to compute than the conventional nonparametric bootstrap, particularly in complex nonlinear models. Despite this computational advantage, in the linear model, the score bootstrap studentized test statistic is equivalent to that of the conventional wild bootstrap up to order `O_p(n^(-1))`. We establish the consistency of the procedure for Wald and Lagrange Multiplier type tests and tests of moment restrictions for a wide class of M-estimators under clustering and potential misspecification. In an extensive series of Monte Carlo experiments we find that the performance of the score bootstrap is comparable to competing approaches despite its computational savings.
Keywords: No keywords provided
JEL Codes: C01; C12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| score bootstrap procedure (C51) | computationally less expensive (C63) |
| score bootstrap's studentized test statistic (C46) | equivalent to conventional wild bootstrap (C59) |
| score bootstrap (C52) | maintains consistency for tests of Wald and Lagrange multiplier types (C51) |
| score bootstrap (C52) | comparable to competing approaches (C52) |
| score bootstrap (C52) | effective in settings where model is computationally expensive to estimate (C51) |