Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Published by Princeton University Press, 2015
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Published by Princeton University Press, 2016
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Published by Princeton University Press, 2016
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Published by Princeton University Press, US, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Hardback. Condition: New. Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Published by Princeton University Press 2016-01-26, 2016
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Published by Princeton University Press, 2015
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Published by Princeton University Press, US, 2015
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Add to basketHardback. Condition: New. Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
Language: English
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Published by Princeton University Press, 2016
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Published by Princeton University Press, 2015
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Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Published by Princeton University Press, 2015
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Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Published by Princeton University Press, 2015
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Published by Princeton University Press, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
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Add to basketCondition: New. Über den AutorEdward P. Herbst & Frank SchorfheideKlappentextrnrnDynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academ.
Published by Princeton University Press, US, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
Language: English
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.
Hardback. Condition: New. Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Published by Princeton University Press Dez 2015, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
Language: English
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Add to basketBuch. Condition: Neu. Neuware - 'This book depicts valuable and revealing methods for solving, estimating, and analyzing a class of dynamic equilibrium models of the macroeconomy. It describes formally tractable techniques for the study of macroeconomic models that feature transition mechanisms for a large number of underlying shocks. Both authors have played important roles in developing and applying these techniques. This is a terrific resource for how to use these methods in practice.'--Lars Peter Hansen, David Rockefeller Distinguished Service Professor of Economics, University of Chicago, and recipient of the Nobel Prize in economics.
Published by Princeton University Press, US, 2015
ISBN 10: 0691161089 ISBN 13: 9780691161082
Language: English
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Add to basketHardback. Condition: New. Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.