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BL
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| Record Number
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845086
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| Main Entry
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Rossi, Peter E., (Peter Eric),1955-
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| Title & Author
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Bayesian non- and semi-parametric methods and applications /\ Peter E. Rossi.
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| Publication Statement
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Princeton :: Princeton University Press,, [2014]
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, ©2014
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| Series Statement
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The econometric and tinbergen institutes lectures
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| Page. NO
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1 online resource (xiii, 202 pages) :: illustrations
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| ISBN
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1400850304
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: 9781400850303
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0691145326
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1306548020
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9780691145327
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9781306548021
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| Bibliographies/Indexes
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Includes bibliographical references (pages 195-200) and index.
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| Contents
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1.1. Finite Mixture of Normals Likelihood Function -- 1.2. Maximum Likelihood Estimation -- 1.3. Bayesian Inference for the Mixture of Normals Model -- 1.4. Priors and the Bayesian Model -- 1.5. Unconstrained Gibbs Sampler -- 1.6. Label-Switching -- 1.7. Examples -- 1.8. Clustering Observations -- 1.9. Marginalized Samplers -- \
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2.1. Dirichlet Processes-A Construction -- 2.2. Finite and Infinite Mixture Models -- 2.3. Stick-Breaking Representation -- 2.4. Polya Urn Representation and Associated Gibbs Sampler -- 2.5. Priors on DP Parameters and Hyper-parameters -- 2.6. Gibbs Sampler for DP Models and Density Estimation -- 2.7. Scaling the Data -- 2.8. Density Estimation Examples.
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3.1. Joint vs. Conditional Density Approaches -- 3.2. Implementing the Joint Approach with Mixtures of Normals -- 3.3. Examples of Non-parametric Regression Using Joint Approach -- 3.4. Discrete Dependent Variables -- 3.5. An Example of Expenditure Function Estimation.
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4.1. Semi-parametric Regression with DP Priors -- 4.2. Semi-parametric IV Models.
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5.1. Introduction -- 5.2. Semi-parametric Random Coefficient Logit Models -- 5.3. An Empirical Example of a Semi-parametric Random Coefficient Logit Model.
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6.1. When Are Non-parametric and Semi-parametric Methods Most Useful? -- 6.2. Semi-parametric or Non-parametric Methods? -- 6.3. Extensions.
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| Abstract
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This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number.
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| Subject
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Bayesian statistical decision theory.
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Econometrics.
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Economics, Mathematical.
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Bayesian statistical decision theory.
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BUSINESS ECONOMICS-- Economics-- General.
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BUSINESS ECONOMICS-- Reference.
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Econometrics.
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Economics, Mathematical.
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| Subject
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Ekonometri.
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| Dewey Classification
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330.01/519542
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| LC Classification
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HB139.R64 2014eb
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| NLM classification
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83.03bcl
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83.03.bcl
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