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:
- Well-established nonparametric and parametric approaches to estimation and conventional (asymptotic and bootstrap) frameworks for statistical inference
- Estimation of models based on moment restrictions implied by economic theory, including various method-of-moments estimators for unconditional and conditional moment restriction models, and asymptotic theory for correctly specified and misspecified models
- Non-conventional asymptotic tools that lead to improved finite sample inference, such as higher-order asymptotic analysis that allows for more accurate approximations via various asymptotic expansions, and asymptotic approximations based on drifting parameter sequences
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.
Table of Contents
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.
Stanislav Anatolyev is Professor at the New Economic School, Moscow. He completed his Ph.D. degree at the University of Wisconsin-Madison in 2000, and now holds a Chair of Access Industries Professor of Economics at the New Economic School. Dr. Anatolyev has published his work in Econometrica, Econometric Theory, Journal of Business and Economic Statistics, Econometric Reviews, and other economic journals.
Nikolay Gospodinov is Associate Professor of Economics at Concordia University, Montreal, and a Research Fellow of CIREQ. He completed his Ph.D. degree at Boston College in 2000. Dr. Gospodinov's previous research has appeared in Econometric Theory, Econometric Reviews, Econometrics Journal, Journal of Business and Economic Statistics, Journal of Econometrics, Journal of Financial Econometrics, and other economic journals.