临近预报
回溯
Lasso(编程语言)
弹性网正则化
机器学习
计算机科学
相关性(法律)
人工智能
体积热力学
人工神经网络
计量经济学
数据挖掘
数学
地理
生态学
物理
量子力学
气象学
万维网
持续性
生物
特征选择
政治学
法学
作者
Daniel Borup,David E. Rapach,Erik Christian Montes Schütte
标识
DOI:10.1016/j.ijforecast.2022.05.005
摘要
We propose an out-of-sample prediction approach that combines unrestricted mixed-data sampling with machine learning (mixed-frequency machine learning, MFML). We use the MFML approach to generate a sequence of nowcasts and backcasts of weekly unemployment insurance initial claims based on a rich trove of daily Google Trends search volume data for terms related to unemployment. The predictions are based on linear models estimated via the LASSO and elastic net, nonlinear models based on artificial neural networks, and ensembles of linear and nonlinear models. Nowcasts and backcasts of weekly initial claims based on models that incorporate the information in the daily Google Trends search volume data substantially outperform those based on models that ignore the information. Predictive accuracy increases as the nowcasts and backcasts include more recent daily Google Trends data. The relevance of daily Google Trends data for predicting weekly initial claims is strongly linked to the COVID-19 crisis.
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