Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton\u2013Perry Method

Jeff Tayman,Jack Baker,David A Swanson

doi:10.1007/s11113-021-09646-7

Abstract

Tayman and Swanson (J Popul Res 34(3):209–231, 2017) found in Washington State counties that a forecast based on the Hamilton–Perry method using a synthetic adjustment (SYN) of cohort change ratios and child-woman ratios had greater accuracy and less bias compared to forecasts holding these ratios constant (CONST). In this paper, we assess the robustness of SYN’s efficacy by evaluating forecast accuracy, bias, and distributional error across age groups in counties nationwide. We also investigate whether forecast errors and their patterns change for SYN and CONST if forecasts by age and gender are adjusted to an independent total population forecast for each county. Our main findings are as follows: (1) SYN lowers forecast error compared to CONST whether the forecasts are controlled or not; (2) controlling also leads to the improvements in forecast error, often exceeding those in SYN; and (3) using SYN and controlling together has the greatest effect in reducing forecast error. These findings remain after controlling for population size and growth rate, but the positive impacts on forecast error of SYN and controlling are most evident in counties with less than 30,000 population and that grow by 15% or more.

Highlights

The general idea of a cohort change ratio (CCR) has been around for at least 100 years (Hardy and Wyatt 1911) and it has been widely used to generate population forecasts since their “re-introduction” by Hamilton and Perry (1962), CCRs have largely remained a tool of applied demographers who generate population forecasts (Smith et al 2013, pp. 176–179)
We examine whether forecast errors and their patterns change for synthetic adjustment (SYN) and CONST by comparing uncontrolled H–P forecasts with H–P forecasts adjusted to an independent total population forecast for each county
We examined the performance of SYN and CONST when the total population forecast was determined from the age-gender forecast and when the age-gender forecasts were controlled to an independent county total population forecast

Summary

Introduction

The general idea of a cohort change ratio (CCR) has been around for at least 100 years (Hardy and Wyatt 1911) and it has been widely used to generate population forecasts since their “re-introduction” by Hamilton and Perry (1962), CCRs have largely remained a tool of applied demographers who generate population forecasts (Smith et al 2013, pp. 176–179). They evaluated 10-year forecasts using a 2000 launch year and a 2010 horizon year, historical data from 1980 to 2000 for each county, and state-level CCRs and CWRs from 2000 and 2010. They found that (1) forecasts based on the CONST were almost universally better (lower error) than forecasts based on TREND; (2) AVG fared much better against CONST; its forecasts, generally had less bias and greater accuracy; (3) SYN outperformed forecasts from TREND and CONST (less bias, greater accuracy, and less allocation error); and (4) SYN outperformed AVG, but to a lesser extent compared with TREND and CONST. We offer suggestions and guidelines for implementing the H–P method in county-level forecasts

Methodology

Summary

Findings

Conclusion