This volume is an early introduction to Bayesian inference and methodology which should still have value for the statistician and student. Coverage ranges from the fundamental concepts and operations of Bayesian inference to analysis of applications in specific econometric problems and a testing of hypotheses and models. As a early work in ...
Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate ...
The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and ...
Bringing together a collection of previously published work, this book provides a timely discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making. Analytical relations between dynamic econometric structural models and ...
How can we manage a so-called "renewable" natural resource such as a fishery when we don't know how renewable it really is? James A. Crutchfield and Arnold Zellner developed a dynamic and highly successful economic approach to this problem, drawing on extensive data from the Pacific halibut industry. Although the US Department of the Interior ...
This is a collection of the author's contributions to the philosophy, theory and application of Bayesian analysis as it relates to statistics, econometrics, and economics. It shows how Bayesians have helped researchers and analysts to become more effective in learning from data and making decisions. Bayesian and non-Bayesian approaches are ...
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