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…mehr
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.
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Autorenporträt
James J. Heckman is the Henry Schultz Distinguished Service Professor of Economics at The University of Chicago. His recent research deals with such issues as evaluation of social programs, econometric models of discrete choice and longitudinal data, the economics of the labor market, and alternative models of the distribution of income. Professor Heckman has received numerous awards for his work, including the John Bates Clark Award of the American Economic Association in 1983, the 2000 Nobel Memorial Prize in Economic Sciences (with Daniel McFadden), the 2005 Jacob Mincer Award for Lifetime Achievement in Labor Economics, the 2005 University College Dublin Ulysses Medal , and the 2005 Aigner award from the Journal of Econometrics.
Inhaltsangabe
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)
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)
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