Much business cycle research is based on an assumption of symmetric cycles, though it is frequently argued that the downturns are steeper and more short-lived than the upturns; implying cyclical asymmetries. A new class of nonlinear autoregressive-asymmetric moving average models is introduced. Thes
Kalman filter estimation for periodic autoregressive-moving average models
โ Scribed by Jimenez, C. ;McLeod, A. I. ;Hipel, K. W.
- Publisher
- Springer
- Year
- 1989
- Tongue
- English
- Weight
- 728 KB
- Volume
- 3
- Category
- Article
- ISSN
- 0931-1955
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
Using the 'standard' approach to forecasting in the vector autoregressive moving average model, we establish basic general results on exact finite sample forecasts and their mean squared error matrices. Comparison between the exact and conditional methods of initiating the finite sample forecast cal
Multiple forecasts for autoregressive-integrated moving-average (ARIMA) models are useful in many areas such as economics and business forecasting. In recent years, approximation methods to construct simultaneous prediction intervals for multiple forecasts arc developed. These methods were based on