Description

Book Synopsis
In this new and expanding area, Tony Lancaster's text is the first comprehensive introduction to the Bayesian way of doing applied economics.

  • Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method;

  • Emphasizes computation and the study of probability distributions by computer sampling;
  • Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data;
  • Details causal inference and inference about structural econometric models;
  • Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software
  • Supported by online supplements, including Data Sets and Solutions to Problems, at

    Trade Review
    “This book conveys the revolution in Bayesian statistics brought about by modern computing and simulation methods from a perspective that econometricians will find familiar. It works through the implications for econometric practice using practical examples and accessible computer software. Graduate students in economics will find it highly accessible. Practitioners steeped in classical econometric methods will find much that is new, exciting, and useful here as well.” John Geweke, University of Iowa


    “Lancaster's text gives an impressive overview of the Bayesian point of view, and should prove a valuable resource to econometricians of all persuasions.” Werner Ploberger, University of Rochester



    Table of Contents
    Introduction.

    1. The Bayesian Algorithm.

    2. Prediction and Model Checking.

    3. Linear Regression.

    4. Bayesian Calculations.

    5. Nonlinear Regression Models.

    6. Randomized, Controlled and Observational Data.

    7. Models for Panel Data.

    8. Instrumental Variables.

    9. Some Time Series Models.

    Appendix 1: A Conversion Manual.

    Appendix 2: Programming.

    Appendix 3: BUGS.

    Index

Introduction to Modern Bayesian Econometrics

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    A Paperback / softback by Tony Lancaster

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      Book details

      Publisher John Wiley and Sons Ltd
      Published 20 April 2004
      ISBN-13 9781405117203
      978-1405117203
      ISBN-10 1405117206
      Also in:
      Econometrics and economic statistics

      Description

      Book Synopsis
      In this new and expanding area, Tony Lancaster's text is the first comprehensive introduction to the Bayesian way of doing applied economics.

      • Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method;

      • Emphasizes computation and the study of probability distributions by computer sampling;
      • Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data;
      • Details causal inference and inference about structural econometric models;
      • Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software
      • Supported by online supplements, including Data Sets and Solutions to Problems, at

        Trade Review
        “This book conveys the revolution in Bayesian statistics brought about by modern computing and simulation methods from a perspective that econometricians will find familiar. It works through the implications for econometric practice using practical examples and accessible computer software. Graduate students in economics will find it highly accessible. Practitioners steeped in classical econometric methods will find much that is new, exciting, and useful here as well.” John Geweke, University of Iowa


        “Lancaster's text gives an impressive overview of the Bayesian point of view, and should prove a valuable resource to econometricians of all persuasions.” Werner Ploberger, University of Rochester



        Table of Contents
        Introduction.

        1. The Bayesian Algorithm.

        2. Prediction and Model Checking.

        3. Linear Regression.

        4. Bayesian Calculations.

        5. Nonlinear Regression Models.

        6. Randomized, Controlled and Observational Data.

        7. Models for Panel Data.

        8. Instrumental Variables.

        9. Some Time Series Models.

        Appendix 1: A Conversion Manual.

        Appendix 2: Programming.

        Appendix 3: BUGS.

        Index

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