Complete and Incomplete Econometric Models

By John Geweke | Go to book overview

3
Prior Predictive Analysis
and Model Evaluation

Prior predictive analysis, described in section 2.4.1, is a versatile tool that provides insight into the characteristics of a model and the means to evaluate a model's adequacy for given data. This chapter illustrates prior predictive analysis and introduces two new techniques: one for studying model characteristics and the other for model evaluation. The emphasis is on the serious application of subjective Bayesian methods, and therefore all of the methods used are consistent with the likelihood principle.

The illustration involves a well-used data set: the monthly Standard & Poor's (S&P) 500 return series. The models considered here are comparatively simple and well-understood: the i.i.d. Gaussian model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and the stochastic volatility (SV) model. The intention is to introduce and illustrate methods in familiar empirical territory, not to break fresh ground with new data or models. Section 3.1 presents the data and models.

The following section begins by undertaking a conventional prior predictive analysis along the lines discussed in section 2.4.1 using several checking functions. This analysis provides some indications of the

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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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