This dissertation presents a new and convenient framework to investigate highdimensional and possibly nonlinear economic processes. It provides a practical toolkit fornonparametric estimation, data visualization, and model specification, thereby enhancingunderstanding of complex economic phenomena.In Chapter I, I develop a nonparametric method to estimate econometric modelswith many input variables and many regression functions. To enable nonparametricregression, I reduce the many input variables to a few inverse regression variates. I thenreduce the many regression functions to a few nonparametric factors. Not only may thesenonparametric factors represent economically meaningful objects, but they also facilitatehigh dimensional data visualization and parametric model specification. I apply this method to study asset pricing and provide new insights into the value and size premia infinancial markets