This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor's degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis.Key Features:Focuses on the assumptions underlying the algorithms rather than their statistical properties.Presents cutting-edge analysis of factor models and finite mixture models.Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately.Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems.Introduces R programming concepts throughout the book.Includes appendices that discuss some of the standard statistical concepts and R programming used in the book. 398 pp. Englisch. N� de ref. del art�culo 9780367255381

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Detalles bibliogr�ficos

T�tulo: Learning Microeconometrics with R

Editorial: CRC Press Dez 2020

A�o de publicaci�n: 2020

Encuadernaci�n: Buch

Condici�n: Neu

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Sinopsis:

This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor’s degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis.

Key Features:

Focuses on the assumptions underlying the algorithms rather than their statistical properties.
Presents cutting-edge analysis of factor models and finite mixture models.
Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately.
Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems.
Introduces R programming concepts throughout the book.
Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.

Acerca del autor:

Chris Adams was born and raised in Melbourne Australia and is a lifelong Carlton supporter. Chris received his PhD in economics from the University of Wisconsin - Madison. He has taught at the University of Vermont and in the Johns Hopkins’ Masters of Applied Economics program. He has 17 years experience working in merger regulation and antitrust for the US Federal Trade Commission. He is currently a Principal Analyst at the US Congressional Budget Office. Chris’ work and research focus on econometrics, empirical industrial organization, pharmaceutical innovation and auctions. His work has been published in various academic journals including The Econometrics Journal, Health Affairs, Health Economics, Marketing Science and Economics Letters. Chris is a colon cancer survivor and a research patient advocate with ECOG-ACRIN. Most importantly, he is a father to CJ and husband to Deena.

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