Robust Frontier and Boundary Estimation: Theory and Application

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This research project aims to develop robust estimation methods for various non- and semiparametric frontier models using the efficient polynomial spline quantile smoothing method. More specifically, the objectives of the proposed research include: to propose an easy-to-use robust estimation procedure for non- and semi-parametric frontier models; to develop an innovative penalized polynomial spline quantile regression for variable selection and efficient estimation of non- and semi-parametric frontier models; to study the asymptotic properties and develop efficient numerical algorithms of the proposed methods. The P.I. also plans to investigate polynomial spline estimations of flexible semiparametric ARCH models and formulate a nonparametric likelihood ratio test based on polynomial spline estimation to make inferences of the coefficient functions in the semiparametric ARCH model.The proposed research generates new methods and theories of curve fitting. The results of the proposed research are very useful for researchers in a wide range of fields. For example, it can be used to enhance the understanding of the impact of the macro economic variables on the stock prices and benefit investment banks to help improve risk management. Furthermore, the proposed research will be incorporated into teaching activities through development of a graduate course on nonparametric methods and time series analysis, which promotes involvement of students in the research of current sciences.