One of the main topics in the development of predictive models is the identification of variables which are predictors of a given outcome. Automated model selection methods, such as backward or forward stepwise regression, are classical solutions to this problem, but are generally based on strong assumptions about the functional form of the model or the distribution of residuals. The quantile regression can give complete information about the relationship between the response variable and covariates on the entire conditional distribution, and has no distributional assumption about the error term in the model.The study aimed to: 1- evaluate the performance of the Lasso regression as a good alternative to ordinary least squares (OLS) and least absolute value (LAV) regression methods when used to estimate the regression coefficients. 2- Demonstrate the efficiency of the Lasso regression when used to select the best subset variables. 3- present a numerical application to demonstrate the efficiency of the Lasso quantile regression when different quantile regression values are used to select the best subset of variables and estimation regression coefficients. The study results showed that Lasso regression is an appropriate model for estimating the parameters and selection of variables. Lasso quantile regression as regularization technique for simultaneous estimation and variable selection methods are often highly time consuming and maybe suffer from instability.