A new method for the estimation of simulated models is presented. It exploits nonparametric least absolute shrinkage and selection operator (Lasso) to find the parameters of a simulation model producing statistics that are close to the ones obtained in real-world data. The simulation model is run for several values of the parameters, statistics are computed on each run, and the function linking the generated statistics and the associated parameters is estimated nonparametrically. Estimates of the parameters are then obtained through the previous nonparametric estimate using real-world statistics as explanatory variables. At odds with simulated minimum-distance techniques (e.g., indirect inference and simulated method of moments), our framework does not involve any explicit objective function, and no optimization algorithm is required. The asymptotic rate of convergence of the estimator to the true value and the error in the estimation of the coefficients and the prediction are explicitly and rigorously characterized. The approach is evaluated through a small simulation study.