Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models

The classical econometric approach to modelling has been to specify a model up to a finite-dimensional parameter vector, and estimation and testing techniques have been widely used on these finite-dimensional parameter spaces. This book provides a survey of these modern techniques and how they are applied to limited dependent variable models.

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The classical econometric approach to modelling has been to specify a model up to a finite-dimensional parameter vector, and estimation and testing techniques have been widely used on these finite-dimensional parameter spaces. In the last fifteen years or so however, new methods have been developed to allow more flexible models which utilise infinite-dimensional parameters. Simultaneously, methods of moments estimation have also become more widely used and applied. In this book, the author provides a survey of these modern techniques and how they are applied to limited dependent variable (LDV) models. As well as covering many classical approaches, the topics covered include: instrumental variable estimation, the generalized method of moments, extremum estimators, methods of simulated moments, minimum distance estimation, nonparametric density and regression function estimation, and semiparametric methods for LDV. As a result, many graduate students and research workers will appreciate this up-to-date account. There is an appendix that describes the use of the software package GAUSS to implement these methods in conjunction with some real data sets.