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This concise textbook is an introduction to econometrics from the Bayesian view-
point. It begins with an explanation of the basic ideas of subjective probability and
shows how subjective probabilities must obey the usual rules of probability to
ensure coherency. It then turns to the definitions of the likelihood function, prior
distributions, and posterior distributions. It explains how posterior distributions are
the basis for inference and explores their basic properties. The Bernoulli distribution
is used as a simple example. 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, which leads to an explanation
of classical and Markov chain Monte Carlo (MCMC) methods of simulation. The
latter is proceeded by a brief introduction to Markov chains.
Edward Greenberg
Edward Greenberg is Professor Emeritus of Economics at Washington Uni-
versity, St. Louis, where he served as a Full Professor on the faculty from
1969 to 2005. Professor Greenberg also taught at the University of Wiscon-
sin, 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, Professor Greenberg
is the coauthor of four books: Wages, Regime Switching, and Cycles (1992),
The Labor Market and Business Cycle Theories (1989), Advanced Economet-
rics (1983, revised 1991), and Regulation, Market Prices, and Process Innova-
tion (1979).
