We highlight a fast subsampling method that can be used to provide valid inference in nonlinear dynamic econometric models. This method is based on the subsampling theory proposed by POLITIS and ROMANO (1994, 1994) which computes an estimator on subsamples of the data and uses these estimates to construct valid inference under very weak assumptions. Fast subsampling directly exploits score functions computed on each subsample and avoids recomputing the estimators for each of them thereby reducing computational time considerably. This method is used to obtain the limit distribution of estimators, possibly simulation based, that admit an asymptotic linear representation with both known and unknown rates of convergence. It can also be used for bias reduction and variance estimation. Monte Carlo experiments demonstrate the desirable performance and vast improvement in numerical speed of the fast subsampling method.