Synopses & Reviews
This book introduces a new generation of statistical econometrics. After linear models leading to analytical expressions for estimators, and non-linear models using numerical optimization algorithms, the availability of high- speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach.
After a brief survey of classical parametric and semi-parametric non-linear estimation methods and a description of problems in which criterion functions contain integrals, the authors present a general form of the model where it is possible to simulate the observations. They then move to calibration problems and the simulated analogue of the method of moments, before considering simulated versions of maximum likelihood, pseudo-maximum likelihood, or non-linear least squares. The general principle of indirect inference is presented and is then applied to limited dependent variable models and to financial series.
Synopsis
Most people in developing countries live in cities, and by the end of the decade only the poorest cities in Africa and Asia will be predominantly rural. In this fully revised and updated edition, the authors describe the urbanization of poverty as well as reasons for migration to the city,
urban survival skills, and the level of political involvement of migrants.
Description
Includes bibliographical references (p. [159]-171) and index.
Table of Contents
1. Introduction and Motivations
2. The Method of Simulated Moments
3. Simulated Maximum Likelihood, Pseudo-maximum Likelihood, and Nonlinear Least Squares Methods
4. Indirect Inference
5. Applications of Limited Dependent Variable Models
6. Applications to Financial Series
7. Applications to Switching Regime Models