Recent Advances in Estimating Nonlinear Models

With Applications in Economics and Finance

Business & Finance, Economics, Econometrics, Statistics
Cover of the book Recent Advances in Estimating Nonlinear Models by , Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9781461480600
Publisher: Springer New York Publication: September 24, 2013
Imprint: Springer Language: English
Author:
ISBN: 9781461480600
Publisher: Springer New York
Publication: September 24, 2013
Imprint: Springer
Language: English

Nonlinear models have been used extensively in the areas of economics and finance. Recent literature on the topic has shown that a large number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and estimating nonlinear time series models, and these have typically taken the form of Threshold Autoregression (TAR) models, Exponential Smooth Transition (ESTAR) models, and Markov Switching (MS) models, among several others. This edited volume provides a timely overview of nonlinear estimation techniques, offering new methods and insights into nonlinear time series analysis. It features cutting-edge research from leading academics in economics, finance, and business management, and will focus on such topics as Zero-Information-Limit-Conditions, using Markov Switching Models to analyze economics series, and how best to distinguish between competing nonlinear models. Principles and techniques in this book will appeal to econometricians, finance professors teaching quantitative finance, researchers, and graduate students interested in learning how to apply advances in nonlinear time series modeling to solve complex problems in economics and finance.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Nonlinear models have been used extensively in the areas of economics and finance. Recent literature on the topic has shown that a large number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and estimating nonlinear time series models, and these have typically taken the form of Threshold Autoregression (TAR) models, Exponential Smooth Transition (ESTAR) models, and Markov Switching (MS) models, among several others. This edited volume provides a timely overview of nonlinear estimation techniques, offering new methods and insights into nonlinear time series analysis. It features cutting-edge research from leading academics in economics, finance, and business management, and will focus on such topics as Zero-Information-Limit-Conditions, using Markov Switching Models to analyze economics series, and how best to distinguish between competing nonlinear models. Principles and techniques in this book will appeal to econometricians, finance professors teaching quantitative finance, researchers, and graduate students interested in learning how to apply advances in nonlinear time series modeling to solve complex problems in economics and finance.

More books from Springer New York

Cover of the book Basics of PET Imaging by
Cover of the book Spontaneous Alternation Behavior by
Cover of the book Sexuality and Aging by
Cover of the book Scalable Multi-core Architectures by
Cover of the book Topics in Fractional Differential Equations by
Cover of the book Pediatric Urology for the Primary Care Physician by
Cover of the book Reviews of Environmental Contamination and Toxicology by
Cover of the book Molecular Determinants of Head and Neck Cancer by
Cover of the book Food Ethics by
Cover of the book Manual of Pulmonary Surgery by
Cover of the book Industrial Crops by
Cover of the book Microsurgery for Cerebral Ischemia by
Cover of the book Dialogical Genres by
Cover of the book Dynamic Behavior of Materials, Volume 1 by
Cover of the book Human Immunodeficiency Virus type 1 (HIV-1) and Breastfeeding by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy