Can Google data help predict French youth unemployment?

被引：86
|
作者：
Fondeur, Y.
Karame, F. [1 ,2 ]
机构：
[1] Le Mans Univ, GAINS TEPP, Le Mans, France
[2] CNRS, FR 3435, F-75700 Paris, France
来源：
ECONOMIC MODELLING | 2013年 / 30卷
关键词：
Google econometrics; Forecasting; Nowcasting; Unemployment; Unobserved components; Diffuse initialization; Kalman filter; Univariate treatment of time series; Smoothing; Multivariate models; OUTPUT; MODEL;
D O I：
10.1016/j.econmod.2012.07.017
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
According to the growing "Google econometrics" literature, Google queries may help predict economic activity. The aim of our paper is to test whether these data can enhance predictions of youth unemployment in France. Because we have weekly series on web search queries and monthly series on unemployment for 15- to 24-year olds, we use the unobserved components approach in order to exploit all available information. Our model is estimated with a modified version of the Kalman filter, taking into account the twofold issue of non-stationarity and multiple frequencies in our data. We find that including Google data improves unemployment predictions relative to a competing model that does not employ search data queries. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：117 / 125
页数：9
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