论文标题
生育预测的协同作用：通过平均模型提高预测精度
Synergy in fertility forecasting: Improving forecast accuracy through model averaging
论文作者
论文摘要
事实证明，生育能力预测的准确性是具有挑战性的，并保证了重新关注。提高准确性的一种方法是通过平均模型结合一组现有模型的优势。模型平均预测是使用经验模型权重得出的，该权重优化了基于历史数据的每个预测范围的预测精度。我们使用17个国家和6个型号的数据将平均模型平均用于预测生育能力。比较了四种模型平均方法：常见主义者，贝叶斯，模型置信度和相等的权重。我们在一到20年的时间内计算单个模型和模型平均点和间隔预测。我们证明，点预测的平均准确度为4-23 \％，间隔预测的平均准确度为3-24 \％，从较长的范围内，频繁主义者和同等重量的接近。英格兰\＆威尔士的数据用于说明预测年龄特异性生育能力的模型。讨论了模型平均生育能力预测的优势和进一步的潜力。由于模型平均预测的准确性取决于单个模型的准确性，因此持续需要开发出更好的生育模型，以用于预测和模型平均。我们得出的结论是，平均模型具有相当大的希望，可以使用现有模型以系统的方式改善生育能力预测，并保证进一步调查。
Accuracy in fertility forecasting has proved challenging and warrants renewed attention. One way to improve accuracy is to combine the strengths of a set of existing models through model averaging. The model-averaged forecast is derived using empirical model weights that optimise forecast accuracy at each forecast horizon based on historical data. We apply model averaging to fertility forecasting for the first time, using data for 17 countries and six models. Four model-averaging methods are compared: frequentist, Bayesian, model confidence set, and equal weights. We compute individual-model and model-averaged point and interval forecasts at horizons of one to 20 years. We demonstrate gains in average accuracy of 4-23\% for point forecasts and 3-24\% for interval forecasts, with greater gains from the frequentist and equal-weights approaches at longer horizons. Data for England \& Wales are used to illustrate model averaging in forecasting age-specific fertility to 2036. The advantages and further potential of model averaging for fertility forecasting are discussed. As the accuracy of model-averaged forecasts depends on the accuracy of the individual models, there is ongoing need to develop better models of fertility for use in forecasting and model averaging. We conclude that model averaging holds considerable promise for the improvement of fertility forecasting in a systematic way using existing models and warrants further investigation.
