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. April (1996) Markov Chain Monte Carlo in Conditionally Gaussian State Space Models. University of New South Wales. Copy available from MCMC preprint service. (Also available via anonymous ftp at ftp.agsm.unsw.edu.au in the file /pub/agsm/stats/papers/indicator.ps.gz)
• 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.