Nonparametric Regression under Alternative Data Environments

Abstract

This paper proposes a nonparametric regression estimator which can accommodate two empirically relevant data environments. The first data environment assumes that at least one of the explanatory variables is discrete. In such an environment, a "cell" approach which consists of partitioning the data and estimating a separate regression for each cell has usually been employed. The second data environment assumes that one needs to estimate a set of regression functions that belong to different experimental units. In both environments the proposed estimator attempts to reduce estimation error by incorporating extraneous data from the other experimental units or cells when estimating the regression function for a given individual experimental unit or cell. Consistency and asymptotic normality of the proposed estimator are established. Its computational simplicity and simulation results demonstrate a strong potential in empirical applications.