Complete and Incomplete Econometric Models

By John Geweke | Go to book overview

2
The Bayesian Paradigm

The Bayesian paradigm provides a powerful and practical structure for managing the risk inherent in decision making. This chapter discusses the elements of this structure, which is standard in the subjective Bayesian approach to inference and decision making. A number of recent texts provide more detailed consideration of this approach in econometrics, including Poirier (1995), Koop (2003), Lancaster (2004), Geweke (2005), Rossi et al. (2005), and Greenberg (2007). This chapter also reviews the Bayesian literature on model evaluation: the effort to assess whether the structure under consideration corresponds to reality. Model evaluation is an inherently difficult question from a Bayesian perspective, and the rest of this monograph explores some ways this can be done using incomplete models.

The essential element of the Bayesian paradigm is a complete model, detailed in section 2.1. A complete model provides a coherent joint probability distribution for the evidence relevant to the decision, unknown parameters or latent variables in the model, and additional factors that will determine the consequences of the decision but are unknown at the time the decision is made. With this distribution in hand, and a utility function for preferences over all possible

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Complete and Incomplete Econometric Models
Table of contents

Table of contents

  • Title Page iii
  • Contents v
  • Series Editors' Introduction vii
  • Preface ix
  • 1: Introduction 1
  • 2: The Bayesian Paradigm 7
  • 3: Prior Predictive Analysis and Model Evaluation 34
  • 4: Incomplete Structural Models 86
  • 5: An Incomplete Model Space 122
  • References 161
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