Durbin & Koopman, Time Series Analysis by State Space Method

[TomDoan]

The attached zip has the examples and data files from Durbin and Koopman, Time Series Analysis by State Space Methods, Oxford University Press, 2001. These are all examples of the use of the DLM instruction for analyzing state space models. The examples through durkp035.rpf all work with a local level model applied to a single data set, demonstrating the various techniques that can be applied with state space models. These are generally fairly simple examples. The remaining examples are quite a bit more complicated, demonstrating more advanced techniques, including several where the state space model with Normal disturbances is used as an approximation to a non-normal dynamic model.

This book is available for purchase from Estima. For more information, see http://www.estima.com/textbook_durbinkoopman.shtml.

durbin-koopman.zip

Examples/data

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Example	Description	RATS Level
durkp012.rpf	DLM-Kalman filter	Intermediate
durkp019.rpf	DLM-Kalman smoothing	Intermediate
durkp021.rpf	DLM-Smoothed disturbances	Intermediate
durkp023.rpf	DLM-Simulations/conditional simulations	Intermediate
durkp025.rpf	DLM-Filtering and smoothing with missing values	Intermediate
durkp027.rpf	DLM-Out of sample forecasting	Intermediate
durkp032.rpf	DLM-Estimation of component variances	Intermediate
durkp035.rpf	DLM-Diagnostic tests	Intermediate
durkp136.rpf	Spline smoothing (regularly spaced data)	Advanced
durkp162.rpf	Structural time series model	Advanced
durkp167.rpf	Bivariate structural time series model	Advanced
durkp169.rpf	ARIMA models using state space techniques	Advanced
durkp172.rpf	Spline smoothing (irregularly spaced data)	Advanced
durkp175.rpf	Stochastic volatility-QMLE	Advanced
durkp230.rpf	DLM-Poisson model	Advanced
durkp233.rpf	DLM-Non-normal observation disturbances	Advanced
durkp236.rpf	Stochastic volatility-exact ML	Advanced
durkp237.rpf	DLM-Binary data	Advanced