New insights on the impact of coefficient instability on ratio-correlation population estimates

In this study we examine the regression-based ratio-correlation method and suggest some new tools for assessing the magnitude and impact of coefficient instability on population estimation errors. We use a robust sample of 904 counties from 11 states and find that: (1) coefficient instability is not a universal source of error in regression models for population estimation and its impact is less than commonly assumed; (2) coefficient instability is not related to bias, but it does decrease precision and increase the allocation error of population estimates; and (3) unstable coefficients have the greatest impact on counties under 20,000 in population size. Our findings suggest that information about the conditions that affect coefficient instability and its impact on estimation error might lead to more targeted and efficient approaches for improving population estimates developed from regression models

Retail Site Selection And Geographic Information Systems

Geographic Information Systems (GIS) are becoming more widely available and interest in their use for ht purpose of site selection is increasing. One major advantage in their usage is that planners are no longer tied to geographic units, such as zip codes, that do not adequately represent market areas. One such GIS is described here in regard to the data needed to make it work and how its output can be utilized in planning for retail development. The particular advantage of this system is that it links a GIS to a demographic database that is updated frequently. Population and household forecasts generated from the system are combined with transportation and purchasing information to generate demand forecasts for retail and restaurant space

New insights on the impact of coefficient instability on ratio-correlation population estimates

In this study we examine the regression-based ratio-correlation method and suggest some new tools for assessing the magnitude and impact of coefficient instability on population estimation errors. We use a robust sample of 904 counties from 11 states and find that: (1) coefficient instability is not a universal source of error in regression models for population estimation and its impact is less than commonly assumed; (2) coefficient instability is not related to bias, but it does decrease precision and increase the allocation error of population estimates; and (3) unstable coefficients have the greatest impact on counties under 20,000 in population size. Our findings suggest that information about the conditions that affect coefficient instability and its impact on estimation error might lead to more targeted and efficient approaches for improving population estimates developed from regression models