意昂3系列講座

2019年第20講 總第583講

題目：Probabilistic household and living arrangement forecasts

主講人🪷：Nico Keilman，挪威奧斯陸大學教授

主持人：Samir KC，意昂3官网亞洲人口研究中心人力資本方向負責人🙋‍♀️、教授

講座時間✌🏽：2019年5月20日，星期一👩🏽‍🔬，10:00-11:30am

講座地點🍠：東區2號樓意昂3体育516

主辦單位💇🏻‍♂️：意昂3官网亞洲人口研究中心暨人口研究所

講座簡介：First, we discuss the shortcomings of deterministic forecasting models, and argue why probabilistic household and living arrangement forecasts are necessary for informed decision-making. Information from probabilistic forecasts allows policy makers, planners, and other forecast users in the fields of housing, energy, social security etc. to take appropriate decisions, because some household variables are more difficult to predict, and hence more uncertain, than others. It also guides them once actual developments start to deviate from the most likely path. New actions or updated plans are unnecessary as long as developments are likely to remain close to the expected future. Next, we review probabilistic household forecasts published since the turn of the century. An important issue is how to evaluate, ex-post facto, the accuracy of these probabilistic forecasts. We introduce the notion of a scoring function, the general idea of which is that a forecast that predicts the actual outcome with high probability should receive a better score than one that predicts the same outcome with lower probability. Scoring functions are useful for evaluating both interval forecasts (given in the form of prediction intervals with pre-specified coverage probability), and forecasts available in the form of a full probability distribution, either analytically or as a sample. Finally, we give empirical results for scoring functions applied to the first known probabilistic household forecasts.

（由於確定性預測模型有無法避免的缺點🧗🏿‍♂️，概率性的家庭戶和居住安排預測以及建構相應評估函數對於更好地決策有不可替代的作用🧹。本此講座首先討論確定性預測模型的缺點以及概率性的家庭戶和居住安排預測的必要性；然後回顧21世紀以來學者們提出的基於概率方法的家庭戶模型，並引入評估函數；最後給出一個將評分函數應用於家庭戶預測的經驗結果📽。）

主講人簡介🧗🏿‍♂️：

Nico Keilman, Department of Economics, University of Oslo.

MSc in applied Mathematics at Delft University of Technology, PhD in Demography at the University of Utrecht. Has worked for more than thirty years in the fields of mathematical demography, modelling of household dynamics, and forecasting methodology. His current interest is in the link between period and cohort mortality.