000			01610 a2200217 4500
003			OSt
005			20231106110103.0
008			190307b xxu||||| |||| 00| 0 eng d
020			_a9781107015319
040			_cIIT Kanpur
041			_aeng
082			_a330.01519542 G829i2
100			_aGreenberg, Edward
245			_aIntroduction to Bayesian econometrics [2nd ed.] _cEdward Greenberg
250			_a2nd ed.
260			_aCambridge _bCambridge University Press _c2013
300			_axix, 249p
520			_aThis 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.
650			_aEconometrics
942			_cBK
999			_c560151 _d560151