Methods for Estimation and Inference in Modern Econometrics provides a comprehensive introduction to a wide range of emerging topics, such as generalized empirical likelihood estimation and alternative asymptotics under drifting parameterizations, which have not been discussed in detail outside of highly technical research papers. The book also addresses several problems often arising in the analysis of economic data, including weak identification, model misspecification, and possible nonstationarity. The book’s appendix provides a review of some basic concepts and results from linear algebra, probability theory, and statistics that are used throughout the book.
Topics covered include:
Offering a unified approach to studying econometric problems, Methods for Estimation and Inference in Modern Econometrics links most of the existing estimation and inference methods in a general framework to help readers synthesize all aspects of modern econometric theory. Various theoretical exercises and suggested solutions are included to facilitate understanding.
Review of Conventional Econometric Methods: Standard Approaches to Estimation and Statistical Inference. Estimation of Moment Condition Models: Generalized Empirical Likelihood Estimators. Estimation of Models Defined by Conditional Moment Restrictions. Inference in Misspecified Models. Higher-Order and Alternative Asymptotics: Higher-Order Asymptotic Approximations. Asymptotics Under Drifting Parameter Sequences. Appendix: Results from Linear Algebra, Probability Theory and Statistics. Index.