Econometric Modeling and Inference / Jean Pierre Florens

pt. I. Statistical methods. Statistical models --
Sequential models and asymptotics --
Estimation by maximization and by the method of moments --
Asymptotic tests --
Nonparametric methods --
Simulation methods --
pt. II. Regression models. Conditional expectation --
Univariate regression --
Generalized least squares method, heteroskedasticity, and multivariate regression --
Nonparametric estimation of the regression --
Discrete variables and partially observed models --
pt. III. Dynamic models. Stationary dynamic models --
Nonstationary processes and cointegration --
Models for conditional variance --
Nonlinear dynamic models --
pt. IV. Structural modeling. Identification and overidentification in structural modeling --
Simultaneity --
Models with unobservable variables.

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.