Chapter 1 Stock Return and Inflation: An Analysis Based on the State-Space Framework.- Chapter 2 Diffusion Index Model Specification and Estimation: Using Mixed Frequency Datasets.- Chapter 3 Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks.- Chapter 4 On the Use of the Flexible Fourier Form in Unit Roots Tests, Endogenous Breaks, and Parameter Instability.- Chapter 5 Testing for a Markov-Switching Mean in Serially-Correlated Data.- Chapter 6 Nonlinear Time Series Models and Model Selection.- Chapter 7 Nonstationarities and Markov Switching Models.- Chapter 8 Has Wealth Effect Changed Over Time? Evidence from Four Industrial Countries.- Chapter 9 A Simple Specification Procedure for the Transition Function in Persistent Nonlinear Times Series Models.- Chapter 10 Small Area Estimation with Correctly Specified Linking Models.- Chapter 11 Forecasting Stock Returns: Does Switching between Models Help?.- Chapter 12 The Global Joint Distribution of Income and Health.
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.
First comprehensive text to feature the most advanced methodologies and nonlinear modeling techniques for economics and finance
Ideal supplement for graduate students and researchers working with time series analysis
Includes contributions from well-known academics and nonlinear modeling experts