𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Monitoring and adjusting forecasts in the presence of additive outliers

✍ Scribed by Steven Hillmer


Publisher
John Wiley and Sons
Year
1984
Tongue
English
Weight
555 KB
Volume
3
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

✦ Synopsis


The effect of an additive outlier upon the accuracy of forecasts derived from extrapolative methods is investigated. It is demonstrated that an outlier affects not only the accuracy of the forecasts at the time of occurrence but also subsequent forecasts. Methods to adjust for additive outliers are discussed. The results of the paper are illustrated with two examples.


πŸ“œ SIMILAR VOLUMES


Testing for ARCH in the presence of addi
✍ Dick Van Dijk; Philip Hans Franses; AndrΓ© Lucas πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 279 KB πŸ‘ 2 views

In this paper we investigate the properties of the Lagrange Multiplier [LM] test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely aected if AOs

Robust Hierarchical Bayes Estimation of
✍ G.S. Datta; P. Lahiri πŸ“‚ Article πŸ“… 1995 πŸ› Elsevier Science 🌐 English βš– 642 KB

A robust hierarchical Bayes method is developed to smooth small area means when a number of covariates are available. The method is particularly suited when one or more outliers are present in the data. It is well known that the regular Bayes estimators of small area means, under normal prior distri

Estimating and forecasting the long-memo
✍ C. Bisognin; S. R. C. Lopes πŸ“‚ Article πŸ“… 2007 πŸ› John Wiley and Sons 🌐 English βš– 584 KB

## Abstract We consider one parametric and five semiparametric approaches to estimate __D__ in SARFIMA (0, __D__, 0)~__s__~ processes, that is, when the process is a fractionally integrated ARMA model with seasonality __s__. We also consider __h__‐step‐ahead forecasting for these processes. We pres