Robust Methods and Asymptotic Theory in Nonlinear Econometrics
Author: Herman J. Bierens
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-10838-2
DOI: 10.1007/978-3-642-45529-2

Table of Contents:

Introduction
Preliminary Mathematics
Nonlinear Regression Models
Nonlinear Structural Equations
Nonlinear Models with Lagged Dependent Variables
Some Applications

1 Introduction -- 1.1 Specification and misspecification of the econometric model -- 1.2 The purpose and scope of this study -- 2 Preliminary Mathematics -- 2.1 Random variables, independence, Borel measurable functions and mathematical expectation -- 2.2 Convergence of random variables and distributions -- 2.3 Uniform convergence of random functions -- 2.4 Characteristic functions, stable distributions and a central limit theorem -- 2.5 Unimodal distributions -- 3 Nonlinear Regression Models -- 3.1 Nonlinear least-squares estimation -- 3.2 A class of nonlinear robust M-estimators -- 3.3 Weighted nonlinear robust M-estimation -- 3.4 Miscellaneous notes on robust M-estimation -- 4 Nonlinear Structural Equations -- 4.1 Nonlinear two-stage least squares -- 4.2 Minimum information estimators: introduction -- 4.3 Minimum information estimators: instrumental variable and scaling parameter -- 4.4 Miscellaneous notes on minimum information estimation -- 5 Nonlinear Models with Lagged Dependent Variables -- 5.1 Stochastic stability -- 5.2 Limit theorem for stochastically stable processes -- 5.3 Dynamic nonlinear regression models and implicit structural equations -- 5.4 Remarks on the stochastic stability concept -- 6 Some Applications -- 6.1 Applications of robust M-estimation -- 6.2 An application of minimum information estimation -- References