The purpose of the project is to change dramatically the methods and software available for studying extreme phenomena in economic regression analysis. More specifically, the project develops feasible and practical inference methods for extremal quantile regressions - the models that describe how regressors X affect the outcome of interest Y in the tails. For example, extremal quantile regressions describe how mothers' socio-economic characteristics (smoking, health-care, education) affect the very low quantiles of birthweights. The project makes it possible to make inferences about such regression relationships. The project develops inference methods for parametric and non-parametric extremal quantile regression models and public software (based on the freeware environment) that implements these methods. The project is strictly focused on the following three parts: (1) Inference methods in linear extremal quantile regression, (2) Inference methods in nonparametric extremal quantile regression, (3) Software package (free and publicly available) which implements (1) and (2). The software will be available as a documented R-package with help files and a vignette. The vignette is the document that will contain a short description of the methods, followed by tutorial examples using interesting data sets.
Broader Impact of the Proposal: As stated above, one of the purposes of the project is to produce public software that implements inference methods for extremal quantile regression. After the software package is available, any practitioner will be able to download and install the package from R-website www.r-project.org and use it in the analysis of any data-set, aided by help files and tutorial examples. It should be mentioned that the reason for choosing R as a programming environment is that it serves as a common, free statistical environment widely used by researchers (econometricians, statisticians, biometricians) and practitioners. This characterizes the broader impact of the proposed project. The project will also have direct educational impact by involving help of two students: one undergraduate student and one graduate student. The graduate student will be a coauthor, and both students will be co-authors and co-developers of the software package.
