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Dynamic Economics (03 Edition)

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Synopses & Reviews

Publisher Comments:

andlt;Pandgt;This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics.In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation.The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.andlt;/Pandgt;

Synopsis:

An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers.

Synopsis:

The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.

Synopsis:

optimization programming models, for students and researchers.

Synopsis:

andlt;Pandgt;An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers. andlt;/Pandgt;

Synopsis:

This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics.

Synopsis:

This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics. In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation. The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.

About the Author

Jérôme Adda is a Lecturer in the Department of Economics at University College, London, and a Research Associate at the Institute of Fiscal Studies.Russell Cooper is Professor in the Department of Economics at the University of Texas, Austin. He was formerly affiliated with Boston University and was a Visiting Scholar in the Research Department of the Federal Reserve Bank of Minneapolis.

Product Details

ISBN:
9780262012010
Author:
Adda, Jerome
Publisher:
MIT Press (MA)
Author:
Cooper, Russell
Author:
Cooper, Russell W.
Location:
Cambridge, Mass.
Subject:
Econometrics
Subject:
Macroeconomics
Subject:
Economics, mathematical
Subject:
Mathematical models
Subject:
Econometric models
Subject:
Economics - General
Edition Description:
Includes bibliographical references and index.
Series:
Dynamic Economics
Series Volume:
107-561
Publication Date:
20030831
Binding:
HARDCOVER
Grade Level:
from 17
Language:
English
Illustrations:
34 illus.
Pages:
293
Dimensions:
9 x 6 in

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Related Subjects

History and Social Science » Economics » General

Dynamic Economics (03 Edition) New Hardcover
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$44.20 In Stock
Product details 293 pages MIT Press - English 9780262012010 Reviews:
"Synopsis" by , An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers.
"Synopsis" by , The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.
"Synopsis" by , optimization programming models, for students and researchers.
"Synopsis" by , andlt;Pandgt;An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers. andlt;/Pandgt;
"Synopsis" by , This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics.
"Synopsis" by , This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics. In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation. The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.
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