Prediction, filtering and smoothing in non‐linear and non‐normal cases using Monte Carlo integration

Journal of Applied Econometrics

Volumn 9, Issue 2, 1994, Pages 163-179

  Tanizaki, H ^a   Mariano, R S ^b  

^a KOBE GAKUIN UNIVERSITY   (Japan)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84986393031     PISSN: 08837252     EISSN: 10991255     Source Type: Journal    
DOI: 10.1002/jae.3950090204     Document Type: Article

References (21)

1. Nonlinear Bayesian estimation using Gaussian sum approximations
2. A Monte Carlo approach to nonnormal and nonlinear state space modeling
3. Application of the Kalman filter to revisions in monthly retail sales estimates
4. Modeling with normal polynomial expansion
5. Bayesian inference in econometric models using Monte Carlo integration
6. The use of preliminary data in econometric forecasting
7. Data revision, reconstruction, and prediction: an application to inventory investment
8. ‘Non‐Gaussian state‐space modeling of nonstationary time series’ (with discussion)
9. Recursive Bayesian estimation using piecewise constant approximations
10. A method of simulated moments for estimation of discrete response models without numerical integration
11. Robustification of Kalman Filter models
12. Monte Carlo approximations in Bayesian decision theory
13. Recursive Bayesian estimation using Gaussian sums
14. Further experience in Bayesian analysis using Monte Carlo integration
15. A comparison of three non‐linear filters