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Handbook of Econometrics
A comprehensive survey of the field of econometrics by some of its leading practitioners
James J. Heckman (Edited by), Edward Leamer (Edited by)
9780444532008, Elsevier Science
Hardback, published 14 December 2007
1056 pages
24 x 16.5 x 4.9 cm, 1.64 kg
As conceived by the founders of the Econometric Society, econometrics is a field that uses economic theory and statistical methods to address empirical problems in economics. It is a tool for empirical discovery and policy analysis. The chapters in this volume embody this vision and either implement it directly or provide the tools for doing so. This vision is not shared by those who view econometrics as a branch of statistics rather than as a distinct field of knowledge that designs methods of inference from data based on models of human choice behavior and social interactions. All of the essays in this volume and its companion volume 6A offer guidance to the practitioner on how to apply the methods they discuss to interpret economic data. The authors of the chapters are all leading scholars in the fields they survey and extend.
Handbook of Econometrics is now available online at ScienceDirect — full-text online from volume 1 onwards.
Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation (James J. Heckman and Edward Vytlacil)
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Economic Estimators to Evaluate Social Programs and to Forecast Their Effects in New Environments (James J. Heckman and Edward Vytlacil)
Econometric Evaluation of Social Programs Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation (Jaap Abbring and James J. Heckman)
Nonparametric Identification (Rosa Matzkin)
Implementing Nonparametric and Semiparametric Estimators (Hidehiko Ichimura and Petra Todd)
The Econometrics of Data Combination (Robert Moffitt and Geert Ridder)
Large Sample Sieve Estimation of Semi-Nonparametric Models (Xiaohong Chen)
Linear Inverse Problems and Structural Econometrics Estimation Based on Spectral Decomposition and Regularization (Marine Carrasco, Jean-Pierre Florens, and Eric Renault)
