Reading List

General Methodology

• Priestley, M.B. (1981) Spectral Analysis and Time Series, Academic Press, London and San Diego.
• Priestley, M.B. (1988) Non-linear and Non-stationary Time Series Analysis, Academic Press, London.
• M. West (1997) Modelling and robustness issues in Bayesian time series analysis (with discussion). In Bayesian Robustness 2, (eds: J. Berger, F. Ruggeri, and L. Wasserman), IMS Monographs (forthcoming). Duke Statistics DP 95-12.

Dynamic state space models -- Methodology

• M. West and P.J. Harrison (February 1997) Bayesian Forecasting and Dynamic Models (2nd Edition). Springer-Verlag, New York.
• A. Pole, M. West and P.J. Harrison (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman-Hall, New York.
• M. West (1997) Bayesian Forecasting. In Encyclopedia of Statistical Sciences, (eds: S. Kotz, C.B. Read, and D.L. Banks), Wiley, New York (forthcoming). Duke Statistics DP 95-11.w
• Geweke, J. and Terui, N. (1993). Bayesian threshold auto-regressive models for nonlinear time series. Journal of Time Series Analysis, 14 (5), p. 441.
• Shepard, N. (1994), Partial non-Gaussian state space, Biometrika, 81, 115-31.
• Jacquier, E., Polson, N.G., and Rossi, P., (1994). Bayesian analysis of stochastic volatility models. Journal of Business and Economics Statistics.
• Prado, Raquel and West, Mike. "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions", Duke Statistics DP 96-13.
• West, M. (1994) Inference on cycles in time series. Journal of the American Statistical Association. Duke Statistics DP 93-A23.
• C. Cargnoni, P. Mueller, and M. West (1997) Bayesian forecasting of multinomial time series through conditionally Gaussian dynamic models. Journal of the American Statistical Association, (forthcoming). Duke Statistics DP 95-22.
• Carter, C.K. and Kohn, R. (1996) "Markov Chain Monte Carlo in conditionally Gaussian State Space Models". Biometrika. 83, 589-601.
• DeJong, P. and Shephard, N. (1995). "The Simulation Smoother for Time Series Models". Biometrika. 82, 339-350.

Dynamic state space models -- Computational issues

• Fruehwirth-Schnatter, S., xyz.
• Albert, J. and Chib, S. (1993). Bayes inference via Gibbs sampling of autoregressive time series subject to markov mean and variance shifts. Journal of Business and Economic Statistics , 11, pp. 1--15.
• Carlin, B.P. Polson, N.G., and Stoffer, D.S. (1992). A Monte Carlo approach to non-normal and non-linear state-space modelling. Journal of the American Statistical Association , 87, pp. 493-500.
• Carter, C.K.} and {\sc Kohn, R. (1994). Bayesian methods for conditionally Gaussian state space models. Technical Report, Australian Graduate School of Management, UNSW.

(G)ARCH and Stochastic Volatility Models

• Bollerslev, T., Chou, R. , and Kroner, K.F. (1992). ARCH modeling in finance, Journal of Econometrics, 52, pp. 5--59.
• Jacquier, E., Polson, N.G., and Rossi, P., (1994). Bayesian analysis of stochastic volatility models Journal of Business and Economics Statistics, 12, 371-389.
• Jacquier, E., Polson, N.G., and Rossi, P., (1995). Models and priors for multivariate stochastic volatility, University of Chicago, Technical Report.
• Tsay, R.S. (1987). Conditional heteroscedastic time series models Journal of the American Statistical Association, 82.

Mixture models in time series models

• Albert, J. and Chib, S. (1993). Bayes inference via Gibbs sampling of autoregressive time series subject to markov mean and variance shifts. Journal of Business and Economic Statistics, 11, 1-15.
• Carter, C.K. and Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81, 541-554.
• Mueller, P., West, M., and MacEachern, S. (1994). Bayesian Models for Non-Linear Auto-Regressions, Duke Statistics DP 94-30.

Applications

• M. West (1996) Some statistical issues in Palaeoclimatology (with discussion). In Bayesian Statistics 5, (eds: J.O. Berger, J.M. Bernardo, A.P. Dawid and A.F.M. Smith), Oxford University Press. Duke Statistics DP 94-09.
• M. West (1997) Bayesian time series: Models and computations for the analysis of time series in the physical sciences. In Maximum Entropy and Bayesian Methods 15, (eds: K. Hanson and R. Silver), Kluwer (forthcoming). Duke Statistics DP 95-20.
• M. West (1995) Time series decomposition and analysis in a study of oxygen isotope records. Duke Statistics DP 95-18.

Preprint service

There is a preprint service on Markov chain Monte Carlo methods: MCMC preprint service.