Estimating trends in the total fertility rate with uncertainty using imperfect data: Examples from West Africa.
Estimating trends in the total fertility rate with uncertainty using imperfect data: Examples from West Africa.
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DOI：
10.4054/demres.2012.26.15
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发表时间：
2012-04-25
影响因子：
2.1
通讯作者：
Pelletier F
中科院分区：
文献类型：
--
作者：
Alkema L;Raftery AE;Gerland P;Clark SJ;Pelletier F
由于数据有限和数据质量参差不齐，估计总生育率对许多发展中国家来说是一项挑战。需要一种标准化的、可重复的方法来产生包括不确定性评估的估计值。我们开发了一种方法来估计和评估总生育率随着时间的推移的不确定性，基于来自不同数据来源的多个不完美的观察，包括调查和人口普查。我们考虑到测量误差的观察分解成偏差和方差，并评估两者的线性回归的各种数据质量协变量。我们使用局部平滑器估计总生育率，并使用加权似然自助法评估不确定性。我们将我们的方法应用于西非七个国家的数据，并构建总生育率的估计值和不确定性区间。基于交叉验证练习，我们发现，占观察之间的数据质量的差异，提供更好的校准置信区间，并减少偏差。在使用来自不同数据来源的多个不完美观测值来估计总生育率或一般人口指标时，应考虑到误差方差的潜在偏差和差异，以改进估计数及其不确定性评估。
Estimating the total fertility rate is challenging for many developing countries because of limited data and varying data quality. A standardized, reproducible approach to produce estimates that include an uncertainty assessment is desired. We develop a method to estimate and assess uncertainty in the total fertility rate over time, based on multiple imperfect observations from different data sources, including surveys and censuses. We take account of measurement error in observations by decomposing it into bias and variance, and assess both by linear regression on a variety of data quality covariates. We estimate the total fertility rate using a local smoother, and assess uncertainty using the weighted likelihood bootstrap. We apply our method to data from seven countries in West Africa and construct estimates and uncertainty intervals for the total fertility rate. Based on cross-validation exercises, we find that accounting for differences in data quality between observations gives better calibrated confidence intervals and reduces bias. When working with multiple imperfect observations from different data sources to estimate the total fertility rate, or demographic indicators in general, potential biases and differences in error variance should be taken into account to improve the estimates and their uncertainty assessment.