Econometric Modeling and Inference

Presents 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.

ISBN 052170006X   Pages 518 ISBN13 9780521700061 (What's this?)   Volumes 1 Publisher Cambridge University Press   Weight (grammes) 682 Imprint Cambridge University Press   Published in Cambridge Format Paperback   Series title Themes in Modern Econometrics Publication date 02 Jul 2007   Height (mm) 228 Translator Josef Perktold, Marine Carrasco   Width (mm) 152 Library of Congress 2006101125   Spine width (mm) 25 DEWEY 330.015195   Academic level Professional / Scholarly DEWEY edition DC22

1		Statistical Models		3
2		Sequential Models and Asymptotics		17
3		Estimation by Maximization and by the Method of Moments		33
4		Asymptotic Tests		61
5		Nonparametric Methods		87
6		Simulation Methods		103
7		Conditional Expectation		129
8		Univariate Regression		141
9		Generalized Least Squares Method, Heteroskedasticity, and Multivariate Regression		179
10		Nonparametric Estimation of the Regression		213
11		Discrete Variables and Partially Observed Models		234
12		Stationary Dynamic Models		261
13		Nonstationary Processes and Cointegration		304
14		Models for Conditional Variance		341
15		Nonlinear Dynamic Models		366
16		Identification and Overidentification in Structural Modeling		395
17		Simultaneity		421
18		Models with Unobservable Variables		446

'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

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