Econometric Modeling and Inference
Econometric Modeling and Inference
Velayoudom Marimoutou, Université d'Aix-Marseille
Anne Peguin-Feissolle, GREQAM, Aix-Marseille
Josef Perktold
Marine Carrasco
$78.99
USDPresents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.
- A graduate text in econometrics and statistics, emphasizing theory and methods, not applications
- Links teaching and recent approaches in research: nonparametric techniques and simulation methods, game theory and treatment effects
- Contains numerous theoretical examples that are solved in the discussion
Reviews & endorsements
'This book is invaluable to researchers and all who are interested in the statistical analysis of time series, microeconomic data, financial and econometric models.' Journal of Applied Statistics
'… this book … make[s] a great contribution to teaching the next generation of theoretical econometricians. … Econometric Modeling and Inference provides an excellent, low- cost opportunity to catch up with what the econometrics subfield has been doing.' Journal of the American Statistical Association
Product details
July 2007Paperback
9780521700061
518 pages
228 × 152 × 25 mm
0.682kg
Temporarily unavailable - available from May 2021
Table of Contents
- Part I. Statistical Methods:
- 1. Statistical models
- 2. Sequential models and asymptotics
- 3. Estimation by maximization and by the method of moments
- 4. Asymptotic tests
- 5. Nonparametric methods
- 6. Simulation methods
- Part II. Regression Models:
- 7. Conditional expectation
- 8. Univariate regression
- 9. Generalized least squares method, heteroskedasticity, and multivariate regression
- 10. Nonparametric estimation of the regression
- 11. Discrete variables and partially observed models
- Part III. Dynamic Models:
- 12. Stationary dynamic models
- 13. Nonstationary processes and cointegration
- 14. Models for conditional variance
- 15. Nonlinear dynamic models
- Part IV. Structural Modeling:
- 16. Identification and over identification in structural modeling
- 17. Simultaneity
- 18. Models with unobservable variables.
