Complete and Incomplete Econometric Models

By John Geweke | Go to book overview

1
Introduction

Models are the venue for expressing, comparing, and evaluating alternative ways of addressing important questions in economics. Applied econometricians are called upon to engage in these exercises using data and, often, formal methods whose properties are understood in decision-making contexts. This is true of work in other sciences as well.

There is a large literature on alternative formal approaches to these tasks, including both Bayesian and non-Bayesian methods. Formal approaches tend to take models as given, and the more formal the approach the more likely this is to be true. Whether the topic is inference, estimation, hypothesis testing, or specification testing, the formal treatment begins with a specific model. The same is also true of formal approaches to the tasks of prediction and policy evaluation.

Yet the ultimate success of these endeavors depends strongly on creativity, insight, and skill in the process of model creation. As the model builder entertains new ideas, casting off those that are not deemed promising and developing further those that are, he or she is engaged in a sophisticated process of learning. This process does not, typically, involve the specification of a great many models developed to the point of

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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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