What do Bayesian methods offer population forecasters
What do Bayesian methods offer population forecasters
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发表时间：
2010-06
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通讯作者：
G. Abel;J. Bijak;J. Forster;J. Raymer;Peter W. F. Smith
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作者：
G. Abel;J. Bijak;J. Forster;J. Raymer;Peter W. F. Smith
贝叶斯方法对于概率预测具有许多有吸引力的特性。在本文中，我们应用贝叶斯时间序列模型，以获得未来的人口估计与不确定性英格兰和威尔士。为了解释历史数据中的异质性，我们添加了参数来表示误差项中的随机波动率。模型选择的不确定性是通过贝叶斯模型平均技术。由此产生的预测分布贝叶斯预测模型有两个主要的优势，使用传统的随机模型。首先，使用概率分布明确地包括参数和模型选择中的数据和不确定性。因此，可以获得更现实的概率人口预测。第二，贝叶斯模型正式允许将专家意见，包括不确定性，纳入预测。我们的研究结果进行了讨论，经典的时间序列方法和现有的队列组件预测。本文展示了简单的人口预测的贝叶斯方法的灵活性，并提供了更复杂的人口模型，包括，例如，人口变化的组成部分的进一步发展的见解。
The Bayesian approach has a number of attractive properties for probabilistic forecasting. In this paper, we apply Bayesian time series models to obtain future population estimates with uncertainty for England and Wales. To account for heterogeneity found in the historical data, we add parameters to represent the stochastic volatility in the error terms. Uncertainty in model choice is incorporated through Bayesian model averaging techniques. The resulting predictive distributions from Bayesian forecasting models have two main advantages over those obtained using traditional stochastic models. Firstly, data and uncertainties in the parameters and model choice are explicitly included using probability distributions. As a result, more realistic probabilistic population forecasts can be obtained. Second, Bayesian models formally allow the incorporation of expert opinion, including uncertainty, into the forecast. Our results are discussed in relation to classical time series methods and existing cohort component projections. This paper demonstrates the flexibility of the Bayesian approach to simple population forecasting and provides insights into further developments of more complicated population models that include, for example, components of demographic change.