Bayesian Full Information Structrual Analysis

Bayesian Theory

This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics

Bayesian full information structural analysis

A Structural Analysis of Expectation Formation

Using panel data of individual firms drawn from French surveys, a structural analysis is developed to study the formation of production plans and the rationality of expectations. The production decision of a firm is defined as the optimal solution of a dynamic stochastic optimization problem. The empirical work amounts to recovering the structural parameters characterizing the model of the firm from estimates of the derived decision rule. The preceding analysis of production plans is based on the assumption that firms are rational. To justify this assumption, direct tests offer evidence that the Rational Expectations Hypothesis may not be rejected for quantity variables.