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Carte Asymmetric Kernel Smoothing: Theory and Applications in Economics and Finance Masayuki Hirukawa

Asymmetric Kernel Smoothing: Theory and Applications in Economics and Finance

Autor: Masayuki Hirukawa, Mari Sakudo
Limbă: engleză
Legare: Carte broșată
Editura: Springer Verlag, Singapore
Disponibilitate: La editor doar la comandă
Expediem în 17-27 zile
294.26 lei
This is the first book to provide an accessible and comprehensive introduction to a newly developed...

Informații despre carte

Autor
Masayuki Hirukawa, Mari Sakudo
Limbă
engleză
Legare
Carte - Carte broșată
Publicat
2018
Pagini
110
EAN
9789811054655
ISBN
9811054657
Enbook ID
16406339
Editura
Springer Verlag, Singapore
Greutate
212
Dimensiuni
234 x 155 x 18

Descriere completă

This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics addressed include the bias-variance tradeoff, smoothing parameter choices, achieving rate improvements with bias reduction techniques, and estimation with weakly dependent data. Further, the large- and finite-sample properties of estimators and test statistics smoothed by asymmetric kernels are compared with those smoothed by symmetric kernels. Lastly, the book addresses the applications of asymmetric kernel estimation and testing to various forms of nonnegative economic and financial data. 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.

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