论文标题
通过棱镜使用Internet搜索数据预测失业
Forecasting unemployment using Internet search data via PRISM
论文作者
论文摘要
从互联网产生的大数据为预测分析提供了巨大的潜力。在这里，我们专注于使用在线用户的Internet搜索数据来预测未来数周的初始索赔，这为经济方向提供了及时的见解。为此，我们提出了一种新颖的方法棱镜（通过推断的季节性模块进行了惩罚回归），该模块使用了Google公开可用的在线搜索数据。 Prism是一种半参数方法，是由一般状态空间配方促进的，并采用了非参数季节性分解和惩罚回归。为了预测失业率的初步索赔，Prism的表现要优于所有以前可用的方法，包括在2008 - 2009年的金融危机期间进行预测，以及在199年间大流行期间的近距离预测，当时失业率最初的索赔都迅速增长。 PRISM的及时，准确的失业预测可以帮助政府机构和金融机构评估经济趋势并做出明智的决定，尤其是面对经济动荡。
Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users' Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method PRISM (Penalized Regression with Inferred Seasonality Module), which uses publicly available online search data from Google. PRISM is a semi-parametric method, motivated by a general state-space formulation, and employs nonparametric seasonal decomposition and penalized regression. For forecasting unemployment initial claims, PRISM outperforms all previously available methods, including forecasting during the 2008-2009 financial crisis period and near-future forecasting during the COVID-19 pandemic period, when unemployment initial claims both rose rapidly. The timely and accurate unemployment forecasts by PRISM could aid government agencies and financial institutions to assess the economic trend and make well-informed decisions, especially in the face of economic turbulence.
