Resources

On this site (under construction), I summarise some popular algorithms found in literature on state space models, Bayesian time series econometrics and econometrics in general. I also provide some implementation-ready code based on the object-oriented matrix language OxMetrics of Doornik (2007) and the library of state space functions SsfPack of Koopman et al. (2008).

1. Linear and Gaussian state space model and the Kalman filter, the Kalman smoother and two simulation smoothers
2. The basic stochastic volatility model (via 7-component mixture and the multi-move sampler)
3. Time-varying parameter regression model with stochastic volatility
4. Stochastic volatility in mean model (with time-varying coefficients)
5. The bootstrap, auxiliary and tempered particle filter
6. Particle Markov chain Monte Carlo and integrated likelihood via particle filter
7. Particle Gibbs (with efficient importance sampling, with ancestor sampling)
8. Interweaving strategy in Meteropolis-Hastings within Gibbs sampler
9. The basic Hamiltonian Markov chain Monte Carlo

Reference:
Doornik, J.A. 2007. Object-Oriented Matrix Programming Using Ox, third ed. Timberlake Consultants Press, London
Koopman, S. J., Shephard, N., Doornik, J. A. 2008. Statistical Algorithms for Models in State Space Form: SsfPack 3.0. Timberlake Consultants Press, London