๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Forecasting with growth curves: The effect of error structure

โœ Scribed by Nigel Meade


Publisher
John Wiley and Sons
Year
1988
Tongue
English
Weight
493 KB
Volume
7
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

โœฆ Synopsis


The logistic model is used for forecasting a number of different types of variable; adoption of durable goods, consumption, growth of human populations. This study investigates the effect of the error structure assumed, on the forecasting accuracy of the logistic. A local logistic model is developed with an additive error term, the variance of which can be made a function of the level of the variable being modelled and its distance from saturation. Evidence is found that suggests that the variance of the disturbance term, when using the logistic to forecast human populations, is proportional to at least the square of population size. Results for the adoption of durable goods are mixed. An error structure consistent with the intuitively reasonable binomial model performed well for some series but not for others. This suggests that some data sets may be inconsistent with the binomial model.


๐Ÿ“œ SIMILAR VOLUMES


The effect of random measurement error o
โœ David Faraggi ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 100 KB ๐Ÿ‘ 2 views

In this paper con"dence intervals for the area under the ROC curve are adjusted for the presence of measurement error. A parametric normal model is assumed. The ratio of intra-individual to inter-individual variance provides a relative measure of the amount of measurement error. An exact adjusted co

Estimation and Prediction of Generalized
โœ Jack C. Lee; Ying-Lin Hsu ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 157 KB

In this paper we consider maximum likelihood analysis of generalized growth curve model with the Box-Cox transformation when the covariance matrix has AR(q) dependence structure with grouping variances. The covariance matrix under consideration is S = D s CD s where C is the correlation matrix with