An Application of Regression and Calibration Estimation to Post-stratification in a Household Survey

This paper empirically compares three estimation methods—regression, calibration, and principal person— used in a household survey for post-stratification. Posttratification is important in many household surveys to adjust for nonresponse and the population undercount that esults from frame deficiencies. The correction for population undercoverage is usually achieved by adjusting estimated people counts in each post-stratum to equal the corresponding population control counts typically available rom an external source such as a census. We will compare estimated means from the three methods and their estimated standard errors for a number of expenditures rom the Consumer Expenditure Survey sponsored by the Bureau of Labor Statistics in an attempt at understanding how each estimation method accomplishes this step in post-stratification.