以下为本人收集的一些Bayesian Econometrics的信息，书名前面加括号的为本人已经得到的书（电子版或纸质），对这个领域有兴趣的人可供参考。

Poirier, D. (1995) Intermediate Statistics and Econometrics: A Comparative Approach.
Cambridge: The MIT Press.
() Zellner, A. (1971) An Introduction to Bayesian Inference in Econometrics. New York:
John Wiley & Sons.
CHETTY,V. K.: "Bayesian Analysis of Some Simultaneous Equation Econometric Models," Ph.D.
Thesis, Department of Economics, University of Wisconsin at Madison, 1966.
Bauwens, L., Lubrano, M. and Richard, J.-F. (1999) Bayesian Inference in Dynamic
Econometric Models. Oxford: Oxford University Press. time series models

http://web.ics.purdue.edu/~jltobias/Links.html
Links to a few Bayesian Econometric/Statistics Texts:

Intermediate Statistics and Econometrics: A Comparative Approach (Poirier, 1995)
() Bayes and Empirical Bayes Methods for Data Analysis (2nd ed) (Carlin and Louis, 2000)
Bayesian Inference in Dynamic Econometric Models (Bauwens, Lubrano and Richard, 2000)
Bayesian Methods: A Social and Behavioral Sciences Approach (Gill, 2002)
() Bayesian Statistical Modeling (Gongdon, 2001)  
() Applied Bayesian Modelling (Congdon, 2003)
() Bayesian Data Analysis (2nd ed) (Gelman, Carlin, Stern and Rubin, 2003)
() Bayesian Econometrics (Koop, 2003)
() An Introduction to Modern Bayesian Econometrics (Lancaster, 2004).
() Contemporary Bayesian Econometrics and Statistics (Geweke, 2005).

The recent book by Tanner (1991) provides explanations of all these algorithms and gives
many illustrative examples.
() Tanner, M. A. (1991), Tools for Statistical Inference, New York: Springer-Verlag.
() Gilks, et al., 1996b, Markov Chain Monte Carlo in Practice
() Gentle, James E. 1998. Random Number Generation and Monte Carlo Methods, New York: Springer-Verlag.
Robert, C. P. and G. Casella. 1999. Monte Carlo Statistical Methods, New York: Springer-Verlag.
Gentle, James E. 2001. Computational Statistics, (in press), New York: Springer-Verlag.

Gelfand, A. E. and A. F. M. Smith. 1990. “Sampling-based approaches to calculating
marginal densities,” Journal of the American Statistical Association, 85: pp. 398-409.
Geman, S. and D. Geman. 1984. “Stochastic relaxation, Gibbs distributions and the
Bayesian restoration of images,” IEEE Transactions PAMI, 6: pp. 721-741.

More directly relevant for econometricians
is Bayesian Analysis, Computation and Communication (BACC), which
handles a wide range of common models (see McCausland and Stevens, 2001).
McCausland, B. and Stevens, J. (2001) Bayesian Analysis, Computation and Communication: The PC MATLAB Version of the BACC
Software. Available at http://www.econ.umn.edu/?bacc/bacc2001.
Bayesian Econometrics,Gary Koop, Department of Economics,University of Glasgow
Accordingly, in addition to discussing computation in detail in the book itself, the website associated with the book
contains MATLAB programs for performing Bayesian analysis in a wide variety of models.
Bayesian data analysis
http://www . stat. columbia. edu/～gelman/
The computations and figures were done using the S, S-Plus, R, and Bugs computer packages (see Appendix C).
www. stat. columbia. edu/ ～gelman/bugsR/
Two statistics texts that use
R extensively are Fox (2002) and Venables and Ripley (2002). Information
about Bugs appears at Spiegelhalter et al. (1994, 2003), and many examples
appear in the textbooks of Congdon (2001, 2003). Several efforts are currently underway to develop Bayesian inference tools using R, for example Martin and Quinn (2002b), Plummer (2003), and
Warnes (2003).
Fox, J. (2002). An R and S-Plus Companion to Applied Regression. Sage.
Spiegelhalter, D., Thomas, A., Best, N., Gilks, W., and Lunn, D. (1994,2003).
BUGS: Bayesian inference using Gibbs sampling. MRC Biostatistics Unit,
Cambridge, England. www.mre-bsu.earn.ae . uk/bugs/
Warnes, G. R. (2003). Hydra: a Java library for Markov chain Monte Carlo.
software.biostat.washington.edu/statsoft/MCMC/Hydra
Plummer, M. (2003). JAGS: a program for analysis of Bayesian graphical
models using Gibbs sampling.
Martin, A. D., and Quinn, K. M. (2002b). MCMCpack.    scythe.wustl.edu/mcmcpack.html
()Congdon, P. (20U1). Bayesian Statistical Modelling. New York: Wiley.
Congdon, P. (2003). Applied Bayesian Modelling. New York: Wiley.

Bayesian Forecasting and Dynamic Models
This can be found at the Duke web site,  http://www.stat.duke.edu/.
Venables et al. (2001) provide an
introduction to R. For a more advanced treatment, see Venables and Ripley (2000).
()Venables, W.N. and Ripley, B.D. (2000) S Programming. New York: Springer-Verlag.
Venables, W.N., Smith, D.M. and R Development Core Team (2001) An Introduction to R.
Bristol: Network Theory Limited.
()Venables, W. N., and Ripley, B. D. (2002). Modern Applied Statistics with S,
fourth edition. New York: Springer-Verlag.
()Krause and Olson, 1997, The basics of S and S-plus
omega.math.albany.edu/jaynesbook.html


BUGS, an acronym for Bayesian Inference Using Gibbs Sampling (see Best et al., 1995),
Best, N., Cowles, M. and Vines, S. (1995) CODA: Manual version 0.30. Available at
http://www.mrc-bsu.cam.ac.uk/bugs/.
Bayesian Analysis, Computation and Communication (BACC), which handles a wide range of common models (see McCausland and Stevens, 2001).
McCausland, B. and Stevens, J. (2001) Bayesian Analysis, Computation and Communication:
The PC MATLAB Version of the BACC Software. Available at http://www.econ.umn.edu/?bacc/bacc2001.
Jim LeSage’s Econometrics Toolbox (see LeSage, 1999) also contains many MATLAB
functions that can be used for aspects of Bayesian inference.
()LeSage, J. (1999) Applied Econometrics Using MATLAB. Available at http://www.spatialeconometrics.
com/.
Geweke, J. (1999a) Computational Experiments and Reality, Department of Economics, University of Iowa working paper
available at www.biz.uiowa.edu/faculty/jgeweke/papers.html.
Griffin, J. and Steel, M.F.J. (2001) Inference with Non-Gaussian Ornstein–Uhlenbeck
Processes for Stochastic Volatility, Institute for Mathematics and Statistics, University
of Kent at Canterbury working paper available at http://www.ukc.ac.uk/IMS/statistics/people/M.F.Steel/.
Sims, C. and Zha, T. (2002) Macroeconomic Switching, Princeton University, Department
of Economics working paper available at http://www.princeton.edu/?sims/.