Paperback. This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language. This textbook is an introduction to econometrics from the Bayesian viewpoint. New material includes a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The R programming language is also emphasized. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de ref. del art�culo 9781107436770

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Detalles bibliogr�ficos

T�tulo: Introduction to Bayesian Econometrics (...

Editorial: Cambridge University Press, Cambridge

A�o de publicaci�n: 2014

Encuadernaci�n: Paperback

Condici�n: new

Edici�n: 2� Edici�n

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Sinopsis:

This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.

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Edward Greenberg is Professor Emeritus of Economics at Washington University, St Louis, where he served as a Full Professor on the faculty from 1969 to 2005. Professor Greenberg also taught at the University of Wisconsin, Madison, and has been a Visiting Professor at the University of Warwick (UK), Technion University (Israel) and the University of Bergamo (Italy). A former holder of a Ford Foundation Faculty Fellowship, Greenberg is the author of the first edition of Introduction to Bayesian Econometrics (Cambridge University Press, 2008) and the co-author of four books: Wages, Regime Switching, and Cycles (1992), The Labor Market and Business Cycle Theories (1989), Advanced Econometrics (1983, revised 1991) and Regulation, Market Prices, and Process Innovation (1979). His published research has appeared in leading journals such as the American Economic Review, Econometrica, the Journal of Econometrics, the Journal of the American Statistical Association, Biometrika and the Journal of Economic Behavior and Organization. Professor Greenberg's current research interests include dynamic macroeconomics as well as Bayesian econometrics.

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