Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support. Yet many types of economic and financial data are nonnegative and violate the presumed conditions of conventional methods. Examples include incomes, wages, short-term interest rates, and insurance claims. Such observations are often concentrated near the boundary and have long tails with sparse data. Smoothing with asymmetric kernel functions has increasingly gained attention, because the approach successfully addresses the issues arising from distributions that have natural boundaries at the origin and heavy positive skewness. Offering an overview of recently developed kernel methods, complemented by intuitive explanations and mathematical proofs, this book is highly recommended to all readers seeking an in-depth and up-to-date guide to nonparametric estimation methods employing asymmetric kernel smoothing.

Provides an accessible collection of recently developed nonparametric smoothing techniques for estimation and testing procedures

1. Asymmetric kernels: definition and history.- 2. Density estimation from nonnegative observations.- 3. Regression estimation with nonnegative regressors.- 4. Model specification tests.- 5. Asymmetric kernel smoothing in action: applications in economics and finance.