Dedy Dwi Prastyo : An Application of Bayesian Adaptive Lasso Quantile Regression to Estimate The Effect of Return to Education on Earning

Dedy Dwi Prastyo S.Si., M.Si.



Published in

The 3rd IndoMS International Conference on Mathematics and Its Applications 2015 (IICMA 2015)

External link



Seminar Internasional


Bayesian quantile regression, adaptive lasso penalty, mincer equation.


Education plays an important role to transfers skill and knowledge toward the increasing in productivity and earning. Using so-called mincer earning function, we investigated the effect of years of schooling, commonly known as return to education, on earning over quantile. By specifying the effect of covariate at different quantile levels we allow the covariate to affect response variable not only at the center of its distribution, but also at its spread. We employed two methods to estimate parameters in mincer equation: (i) Bayesian quantile regression (BQR) and (ii) Bayesian quantile regression with adaptive least absolute shrinkage and selection operator (Lasso) penalty (BALQR). The latter method extends the bayesian Lasso penalty term by employing different penalty function with an adaptive tuning parameter accomodated in the inverse gamma prior distribution. Data used in this paper is samples from workers in agricultural sector in South Sulawesi. Empirical results showed that BALQR outperformed over BQR because it resulted in smaller mean squared error (MSE). In addition, the estimators of the coefficient corresponding to the return to education variable do not monotonically increase over quantile.