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FRIDAYS, 11-12, Economics Conference Room, SSH1131 |
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First Meeting: October 14 |
Koop, Gary (2003) Bayesian Econometrics, Wiley. The book is not currently available at the bookstore nor at the main library (it is available in the ARE Library). It is probably easiest to purchase it from Amazon.
The website for the book contains all the data sets and MATLAB code.
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October 21 meeting: read chapter 1 on your own and try to do the exercises. We will discuss chapter 2, which introduces linear regression from a Bayesian perspective. |
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October 28 meeting: by now you should have read chapters 1-3. It would not hurt to have read chapter 4. We will emphasize computational Monte Carlo techniques. Check the handouts and examples we have prepared. |
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November 4 meeting: we will finish discussing computational MCMC techniques. These are scattered in chapters 3, 4, and 5. |
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November 18 meeting: you should read chapter 9. This is a jump in the order of the chapters but a natural extension of the linear regression model with latent variables (e.g. probit). We will see how bayesian methods can be used for data augmentation. Also, please note Colin has prepared notes and Stata code for the probit example. Please check the handouts section. |
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November 25 meeting: Thanksgiving! |
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December 9 meeting: Tim Cogley will discuss Bayesian techniques for time series models. Tim has experience estimating time-varying parameter VARs and other models. In preparation for that meeting, Tim has asked that you read the following paper: "Has Inflation Become Harder to Forecast" by Stock and Watson, JMCB (2005) |
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Chapter 1: Brief notes and exercises. Exercise solutions. |
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Chapter 2: Notes on the role of the prior; other updating examples (by Jim Chalfant) |
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Chapter 3: Introduction to Bayesian Computation; Gibbs sampler example 1 (in GAUSS); Gibbs sampler example 1 (in STATA) and sample output (STATA) |
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Chapters 1-4: Summary of important concepts so far |
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Chapters 1-9: Revised and updated summary of important concepts. Stata code for Probit example with output file. |
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October 28 at 2pm in IGA: "A Pragmatic Justification for the Use of
Bayesian Methods in the Social Sciences" by Andrew Martin, Washington University of St. Louis. Click here to download his slides. |
According to Koop, most Bayesian econometricians use MATLAB, which are available both in Econ and ARE. MATLAB is a matrix language, similar to GAUSS. GAUSS users should have low barriers in making the switch (in fact there are some gauss-to-matlab converters on the web). Here is a list of the leading resources available for free:
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Bayesian Analysis, Computation and Communication (BACC): This website contains free Bayesian routines that work in Matlab, S-Plus, and R. There are some older version that will work with GAUSS as well, available upon request. The software is part of an NSF project led by Siddharta Chib and John Geweke, two leading expert in Bayesian Econometrics. |
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Bayesian Inference Using the Gibbs Sampler (BUGS): This is a stand alone program, the latest version of which has a windows interface. There is a short movie available that shows how this interface works. Akin to Eviews in terms of having canned procedures at the ready, probably less useful if you want to estimate a specific model not pre-canned. |
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Gary Koop's MATLAB code |
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James LeSage's MATLAB routines (includes other classical routines) |
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Andrew Martin's MCMCpack (works with R) |
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An Introduction to Modern Bayesian Econometrics by Tony Lancaster (Blackwell). This is another good introductory textbook. The book subscribes more strongly to the Bayesian point of view. There are numerous exercises and examples in winBUGS (the windows version of BUGS). |
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Bayesian Data Analysis by Gelman, Carlin, Stern, and Rubin (Chapman and Hall). This is a higher level book from a more statistical perspective. The website for the book contains the solutions to exercises and examples of programs in BUGS. |
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Using Simulation Methods for Bayesian Econometric Methods: Inference, Development and Communication by John Geweke, Econometric Reviews (1999) |
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Jim Hamilton's (UCSD) introductory class notes |
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Andrew Martin's (WUSTL): website; Monte Carlo movie; Gibbs sampler movie; Metropolis-Hastings movie |