论文信息 - Fast likelihood evaluation and prediction for nonstationary state space models

Fast likelihood evaluation and prediction for nonstationary state space models

SUMMARY A recursive procedure for initializing the Kalman filter is displayed. The recursion is for nonstationary state space models. The procedure imposes small computational and programming burden over and above the Kalman filter. The procedure is superior to other suggested approaches in both computational speed and general applicability. General properties of the method are investigated. Details of the initialization for the ARIMA (p, d, q) and basic structural models are considered.

Piet de Jong | Singfat Chu-Chun-Lin | P. D. Jong | S. Chu-chun-lin

[1] B. Anderson,et al. Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[2] R. Kohn,et al. Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data , 1986 .

[3] Piet de Jong,et al. STABLE ALGORITHMS FOR THE STATE SPACE MODEL , 1991 .

[4] Andrew Harvey,et al. Forecasting, Structural Time Series Models and the Kalman Filter. , 1991 .

[5] Robert Kohn,et al. FILTERING AND SMOOTHING IN STATE SPACE MODELS WITH PARTIALLY DIFFUSE INITIAL CONDITIONS , 1990 .

[6] Andrew Harvey,et al. Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[7] Andrew Harvey,et al. Maximum likelihood estimation of regression models with autoregressive-moving average disturbances , 1979 .

[8] Andrew Harvey,et al. Estimating Missing Observations in Economic Time Series , 1984 .

[9] R. Kohn,et al. Estimation, Filtering, and Smoothing in State Space Models with Incompletely Specified Initial Conditions , 1985 .

[10] Steven C. Hillmer,et al. U.S. Census Bureau , 1991 .

[11] P. D. Jong. The Diffuse Kalman Filter , 1991 .