UC Riverside

Nowadays, with advanced technology, it is easier to obtain data like never before. With more available data, comes new information that economists can extract to uncover relationships between economic variables. By using new state of the art machine learning algorithms and techniques that can handle data efficiently and can identify trends and patterns easily, we can help solve economic problems, theoretically and empirically. The primary goal of this dissertation is to help bridge the gap between machine learning and econometrics. With powerful machine learning models that exhibit great predictive ability, it would be useful to further explore machine learning methods and add them to our econometrics toolbox. In addition, we wish to extend these models to incorporate problems often faced in econometric models, including partial effects estimation using first derivatives, evaluating concavity of various economic functions using second derivatives, and allowing for heteroskedastic and autocorrelated errors in an econometric model. These issues are clearly often faced in economics, but not so much in machine learning. To incorporate machine learning techniques, machine learning estimation of nonparametric models and marginal effects are established throughout the dissertation. A derivative estimation procedure of smoothing weighted difference quotients based on random forest is proposed. The procedure of smoothing weighted difference quotients based on random forest is then used to estimate second derivatives. Lastly, a generalized framework for Kernel Regularized Least Squares that incorporates information in the error covariance when estimating the regression function is proposed.