Bibliothèque de L'IMIST/CNRST

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.

This edited volume provides a timely overview of nonlinear estimation techniques, offering new methods and insights into nonlinear time series analysis. The focus is on such topics as state-space model and the identification issue, use of Markov Switching Models and Smooth Transition Models to analyze economic series, and how best to distinguish between competing nonlinear models. Most economic theory suggests that the economic relationships among economic variables in the real world are fairly complex and nonlinear. Nonlinear models are necessary to capture these important channels through which economic variables can influence each other and various policies can affect economic activities. This volume features cutting-edge research from leading academics in economics, finance, and business management. The principles and techniques used here 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.