Description
Book SynopsisReviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. This book advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals.
Trade Review"As the creator of bayesm (R software for Bayesian inference) and lead author of Bayesian Statistics and Marketing, Rossi has deep knowledge of the book's titular methods."--Choice
Table of ContentsPreface vii 1 Mixtures of Normals 1 1.1 Finite Mixture of Normals Likelihood Function 6 1.2 Maximum Likelihood Estimation 9 1.3 Bayesian Inference for the Mixture of Normals Model 15 1.4 Priors and the Bayesian Model 16 1.5 Unconstrained Gibbs Sampler 25 1.6 Label-Switching 29 1.7 Examples 34 1.8 Clustering Observations 46 1.9 Marginalized Samplers 49 2 Dirichlet Process Prior and Density Estimation 59 2.1 Dirichlet Processes--A Construction 60 2.2 Finite and Infinite Mixture Models 64 2.3 Stick-Breaking Representation 68 2.4 Polya Urn Representation and Associated Gibbs Sampler 70 2.5 Priors on DP Parameters and Hyper-parameters 72 2.6 Gibbs Sampler for DP Models and Density Estimation 78 2.7 Scaling the Data 80 2.8 Density Estimation Examples 81 3 Non-parametric Regression 90 3.1 Joint vs. Conditional Density Approaches 90 3.2 Implementing the Joint Approach with Mixtures of Normals 93 3.3 Examples of Non-parametric Regression Using Joint Approach 96 3.4 Discrete Dependent Variables 104 3.5 An Example of Expenditure Function Estimation 108 4 Semi-parametric Approaches 115 4.1 Semi-parametric Regression with DP Priors 115 4.2 Semi-parametric IV Models 122 5 Random Coefficient Models 152 5.1 Introduction 152 5.2 Semi-parametric Random Coefficient Logit Models 157 5.3 An Empirical Example of a Semi-parametric Random Coefficient Logit Model 161 6 Conclusions and Directions for Future Research 187 6.1 When Are Non-parametric and Semi-parametric Methods Most Useful? 187 6.2 Semi-parametric or Non-parametric Methods? 189 6.3 Extensions 191 Bibliography 195 Index 201
