A unified theory of estimation and inference for nonlinear dynamic models

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A unified theory of estimation and inference for nonlinear dynamic models

A. Ronald Gallant and Halbert White

B. Blackwell, 1988

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Includes index

Description and Table of Contents

Description

Building on the great advances that have been made by statisticians and econometricians in the last 40 years, this study presents a unified theory of estimation and inference applicable to a variety of econometric estimators of the parameters of time-dependant heterogenous economic phenomena. The authors begin with a discussion of the underlying data generation processes, establishing the existence and consistency of the estimators of interest (including maximum likelihood method of moments, and m-estimators), under general conditions. They then move on to examine the property of near epoch dependence, the asymptotic normality of the estimators, and consistent estimators for the asymptotic covariance matrix. The book concludes with a presentation of the unified theory of inference and a consideration of directions for further research. The book should be of interest to researchers in economic and econometric theory and statistical modelling.

Table of Contents

2. The data generation process and optimization estimators 3. Consistency of optimization estimators 4. More on near epoch dependence 5. Asymptotic mormality 6. Estimating asymptotic cavariance matrices 7. Hypothesis testing

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