E-Book, Englisch, 234 Seiten
E-Book, Englisch, 234 Seiten
ISBN: 978-1-4398-3826-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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.
Zielgruppe
Econometricians, statisticians, and graduate students in statistics and econometrics.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Review of Conventional Econometric Methods
Standard Approaches to Estimation and Statistical Inference
Introduction
Parametric Estimators
Long-Run Variance
Nonparametric Regression
Hypothesis Testing and Confidence Intervals
Bootstrap Inference
Estimation of Moment Condition Models
Generalized Empirical Likelihood Estimators
Introduction
Empirical Likelihood and Generalized Empirical Likelihood
Relation of GEL to Other Methods and Notions
GEL for Time Series Data
Bias Properties of Method of Moments Estimators
Appendix: Solutions to Selected Exercises
Estimation of Models Defined by Conditional Moment Restrictions
Introduction
Optimal Instruments
Alternative Approaches
Appendix: Solutions to Selected Exercises
Inference in Misspecified Models
Introduction
Quasi-Maximum Likelihood
Pseudo Likelihood Methods
Comparison of Misspecified Models
Appendix: Solutions to Selected Exercises
Higher-Order and Alternative Asymptotics
Higher-Order Asymptotic Approximations
Introduction
Stochastic Expansions
Higher-Order Approximations of Sampling Distributions
Appendix: Solutions to Selected Exercises
Asymptotics Under Drifting Parameter Sequences
Introduction
Weak Identification and Many Instruments
Local-to-Unity and Local-to-Zero Parameterizations in Nearly Nonstationary Models
Appendix: Solutions to Selected Exercises
Appendix
Results from Linear Algebra, Probability Theory and Statistics
Spaces and Norms
Matrix Notation and Definitions
Inequalities
Some Distributional Results
Convergence of Sequences of Random Variables
Orders of Magnitude
Laws of Large Numbers
Central Limit Theorems
Characteristic and Cumulant Generating Functions
Dependent Sequences
Nonstationary Processes
Index
