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  3. Econometric Modeling and Inference
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  • Cited by 16
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2007
Online ISBN:
9780511805592
DOI:
https://doi.org/10.1017/CBO9780511805592
Subjects:
Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability
Series:
Themes in Modern Econometrics
Information

Book description

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.

Reviews

'This book is invaluable to researchers and all who are interested in the statistical analysis of time series, microeconomic data, financial and econometric models.'

Source: 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.'

Source: Journal of the American Statistical Association

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