BACKGROUND
Short-term forecasts of international migration are often based on data that are incomplete, biased, and reported with delays. There is also a scarcity of migration forecasts based on combined traditional and new forms of data.

OBJECTIVE
This research assessed an inclusive approach of supplementing official migration statistics, typically reported with a delay, with the so-called big data from Google searches to produce short-term forecasts (“now-casts”) of immigration flows from Romania to the United Kingdom.

METHODS
Google Trends data were used to create composite variables depicting the general interest of Romanians in migrating into the United Kingdom. These variables were then assessed as predictors and compared with benchmark results by using univariate time series models.

RESULTS
The proposed Google Trends indices related to employment and education, which exhaust all possible keywords and eliminate language bias, match trends observed in the migration statistics. They are also capable of moderate reductions in prediction errors.

CONCLUSIONS
Google Trends data have some potential to indicate up-to-date current trends of interest in mobility, which may serve as useful predictors of sudden changes in migration. However, these data do not always improve the accuracy of forecasts. The usability of Google Trends is also limited to short-term migration forecasting and requires understanding of contexts surrounding origin and destination countries.



中文翻译：

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使用 Google 搜索引擎数据，现在正在投射罗马尼亚移民到英国（作者：Andreea Avramescu、Arkadiusz Wiśniowski）

背景
国际移民的短期预测通常基于不完整、有偏见且报告有延迟的数据。还缺乏基于传统数据和新数据形式的迁移预测。

目标
本研究评估了一种补充官方移民统计数据的包容性方法，该方法通常会延迟报告，并使用来自 Google 搜索的所谓大数据来生成从罗马尼亚到美国的移民流的短期预测（“现在预测”）。王国。

方法
谷歌趋势数据被用来创建复合变量，描述罗马尼亚人移民英国的普遍兴趣。然后将这些变量作为预测变量进行评估，并通过使用单变量时间序列模型与基准结果进行比较。

结果
拟议的与就业和教育相关的谷歌趋势指数，穷尽所有可能的关键词并消除语言偏见，与迁移统计中观察到的趋势相匹配。它们还能够适度减少预测误差。

结论
谷歌趋势数据有一些潜力表明当前对流动性感兴趣的最新趋势，这可能作为迁移突然变化的有用预测指标。然而，这些数据并不总能提高预测的准确性。谷歌趋势的可用性也仅限于短期迁移预测，需要了解原籍国和目的地国的背景。