A modified logistic model applied to human populations

Area of Science:

• Demography
• Statistical Modeling
• Population Dynamics

Background:

• The logistic curve is a common tool for forecasting human populations.
• Previous applications, like Leach's, focused on fitting a logistic trend.
• Assumptions about disturbance variance in these models impact forecasting accuracy.

Purpose of the Study:

• To re-examine the logistic curve's application in United Kingdom population forecasting.
• To propose a modified logistic model for improved population prediction.
• To address limitations in the variance assumptions of existing models.

Main Methods:

• Re-evaluation of Leach's logistic curve model for population forecasting.
• Modification of the logistic model to emphasize forecast trend provision.
• Replacement of the constant additive disturbance variance assumption with constant proportional disturbance variance.

Main Results:

• The modified logistic model demonstrates superior forecasting performance compared to Leach's model.
• The assumption of constant proportional disturbance variance is more realistic for population data.
• The revised approach shifts focus from trend fitting to forecast trend provision.

Conclusions:

• The modified logistic model provides a more accurate and realistic approach to population forecasting.
• The assumption regarding disturbance variance significantly influences model performance.
• This revised method enhances the utility of the logistic curve for demographic predictions.