A regression approach to estimating the average number of persons per household

Entropy Based Modelling for Estimating Demographic Trends

In this paper, an entropy-based method is proposed to forecast the demographical changes of countries. We formulate the estimation of future demographical profiles as a constrained optimization problem, anchored on the empirically validated assumption that the entropy of age distribution is increasing in time. The procedure of the proposed method involves three stages, namely: 1) Prediction of the age distribution of a country's population based on an "age-structured population model"; 2) Estimation the age distribution of each individual household size with an entropy-based formulation based on an "individual household size model"; and 3) Estimation the number of each household size based on a "total household size model". The last stage is achieved by projecting the age distribution of the country's population (obtained in stage 1) onto the age distributions of individual household sizes (obtained in stage 2). The effectiveness of the proposed method is demonstrated by feeding real world data, and it is general and versatile enough to be extended to other time dependent demographic variables.

Can knowledge improve population forecasts at subcounty levels?

Recent developments in urban and regional planning require more accurate population forecasts at subcounty levels, as well as a consideration of interactions among population growth, traffic flow, land use, and environmental impacts. However, the extrapolation methods, currently the most often used demographic forecasting techniques for subcounty areas, cannot meet the demand. This study tests a knowledge-based regression approach, which has been successfully used for forecasts at the national level, for subcounty population forecasting. In particular, this study applies four regression models that incorporate demographic characteristics, socioeconomic conditions, transportation accessibility, natural amenities, and land development to examine the population change since 1970 and to prepare the 1990-based forecast of year 2000 population at the minor civil division level in Wisconsin. The findings indicate that this approach does not outperform the extrapolation projections. Although the regression methods produce more precise projections, the least biased projections are often generated by one of the extrapolation techniques. The performance of the knowledge-based regression methods is discounted at subcounty levels by temporal instability and the scale effect. The regression coefficients exhibit a statistically significant level of temporal instability across the estimation and projection periods and tend to change more rapidly at finer geographic scales.