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Introduction to Bayesian econometrics

By: Greenberg, Edward.
Publisher: Cambridge Cambridge University Press 2013Edition: 2nd ed.Description: xix, 249p.ISBN: 9781107015319.Subject(s): EconometricsDDC classification: 330.01 | G82i2 Summary: 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.
List(s) this item appears in: New arrival March 04 to 10, 2019
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Text Books Text Books P K Kelkar Library, IIT Kanpur
TEXT 330.01519542 G829i2 (Browse shelf) cop.1 Available A178379
Text Books Text Books P K Kelkar Library, IIT Kanpur
TEXT 330.01519542 G829i2 (Browse shelf) cop.2 Available A178380
Text Books Text Books P K Kelkar Library, IIT Kanpur
TEXT 330.01 G82i2 cop.1 (Browse shelf) cop.3 Available A184331
Text Books Text Books P K Kelkar Library, IIT Kanpur
TEXT 330.01 G82i2 cop.2 (Browse shelf) cop.4 Available A184332
Text Books Text Books P K Kelkar Library, IIT Kanpur
TEXT 330.01 G82i2 cop.3 (Browse shelf) cop.5 Available A184333
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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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