Jean Pierre Florens – Econometric Modeling & Inference

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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

Contents

Frontmatter pp i-viii
Contents pp ix-xvi
Foreword pp xvii-xviii
Preface pp xix-xxii

I – Statistical Methods pp 1-2

• 1 – Statistical Models pp 3-16 
• 2 – Sequential Models and Asymptotics pp 17-32
• 3 – Estimation by Maximization and by the Method of Moments pp 33-60
• 4 – Asymptotic Tests pp 61-86
• 5 – Nonparametric Methods pp 87-102
• 6 – Simulation Methods pp 103-126

II – Regression Models pp 127-128

• 7 – Conditional Expectation pp 129-140
• 8 – Univariate Regression pp 141-178
• 9 – Generalized Least Squares Method, Heteroskedasticity, and Multivariate Regression pp 179-212 
• 10 – Nonparametric Estimation of the Regression pp 213-233 
• 11 – Discrete Variables and Partially Observed Models pp 234-258

III – Dynamic Models pp 259-260

• 12 – Stationary Dynamic Models pp 261-303 
• 13 – Nonstationary Processes and Cointegration pp 304-340
• 14 – Models for Conditional Variance pp 341-365
• 15 – Nonlinear Dynamic Models pp 366-392

IV – Structural Modeling pp 393-394

• 16 – Identification and Overidentification in Structural Modeling pp 395-420
• 17 – Simultaneity pp 421-445
• 18 – Models with Unobservable Variables pp 446-476

Bibliography pp 477-492
Index pp 493-496

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