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Bayesian econometrics

Dynamic linear models
Stochastic volatility models

Local level model: Bayesian linear regression vs FFBS (R code)

toy-dlm.R
dlm.R
nldm.R
ar1plusnoise.R

Stochastic volatility model
stochasticvolatilitymodels-R.txt

A few references

• · Aggarwal, Reena, Inclan, Carla and Leal, Ricardo (1999), “Volatility in emerging stock markets,” Journal of Financial and Quantitative Analysis, 34, 33-55.
• · Berg, A., Meyer, R., and Yu. J. (2004), “Deviance Information Criterion for Comparing Stochastic Volatility Models”, Journal of Business and Economic Statistics, 22, 107-20.
• · Carlin, Polson and Stoffer (1992) A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association, 87, 493-500.
• · Carter and kohn (1994) On Gibbs sampling for state space models, Biometrika, 81, 541-553.
• · Chib S., Nardari, F. and Shephard, N. (2002), Markov chain Monte Carlo methods for stochastic volatility models”, Journal of Econometrics, 108, 281-316.
• · de Jong and Shephard (1995) The simulation smoother for time series models, Biometrika, 82, 339-350.
• · Durbin and Koopman (2000) Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives, JRSS-B, 62, 3-56.
• · Durbin and Koopman (2001) Time Series Analysis by State Space Methods. New YorkOxford University Press.
• · Fruhwirth-Schnatter (1994) Data augmentation and dynamic linear models, Journal of Time Series Analysis, 15, 183-102.
• · Gamerman and Lopes (2006) Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (2^nd edition). Boca raton: Chapman & Hall/CRC Press.
• · Ghysels, E., Harvey, A. C., and Renault, E. (1996), “Stochastic Volatility,” in Statistical Models in Finance, eds. C. R. Rao and G. S. Maddala, Amsterdam: North-Holland, pp. 119–91.
• · Harvey (1989) Forecasting, Structural Time Series Models and Kalman Filter. Cambridge: Cambridge University Press.
• · Jacquier, E., Polson, N. G. and Rossi, P. E. (1994) Bayesian analysis of stochastic volatility models (with discussion). Journal of Business and Economic Statistics, 12, 371-415.
• · Kim, S., Shephard, N., and Chib, S. (1998), “Stochastic volatility: Likelihood inference and comparison with ARCH models”, Review of economic studies, 65, 361–93.
• · Migon, Gamerman, Lopes and Ferreira (2005) Dynamic models. In Dey and Rao (Eds.) Handbook of Statistics, Volume 25.
• · Shephard, N. (1996), “Statistical Aspects of ARCH and Stochastic Volatility”, in Time Series Models in Econometrics, Finance and Other Fields, eds. D. R. Cox, D. V. Hinkley, and O. E. Barndoff-Nielson, London: Chapman and Hall, pp. 1–67.
• · Shephard and Pitt (1997) Likelihood analysis of non-Gaussian measurement time series, Biometrika, 84, 653-667.
• · Shephard, N. (2005) Stochastic Volatility: Selected Readings. Oxford University Press, Oxford.
• · West and Harrison (1989/1997) Bayesian Forecasting and Dynamic Models. New York: Springer-Verlag.

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