Previous studies have demonstrated both large efficiency gains and reductions in bias by incorporating population information in regression estimation with sample survey data. These studies, however, assume the population values are exact. This assumption is relaxed here through a Bayesian extension of constrained Maximum Likelihood estimation, applied to 1990s Hispanic fertility. Traditional elements of subjectivity in demographic evaluation and adjustment of survey and population data sources are quantified by this approach, and the inclusion of a larger set of objective data sources is facilitated by it. Compared to estimation from sample survey data only, the Bayesian constrained estimator results in much greater precision in the age pattern of the baseline fertility hazard and, under all but the most extreme assumptions about the uncertainty of the adjusted population data, substantially greater precision about the overall level of the hazard.
This paper series was made possible by the NIA funded RAND Center for the Study of Aging and the NICHD funded RAND Population Research Center.
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