We develop a nowcasting framework, based on microlevel data, to provide faster estimates of the Finnish monthly real economic activity indicator, the Trend Indicator of Output (TIO), and of quarterly GDP. We use firm-level turnovers, which are available shortly after the end of the reference month, and real-time traffic volumes data, to form our set of predictors. We rely on combinations of nowcasts obtained from a range of statistical models and machine learning techniques which are able to handle high-dimensional information sets. The results of our pseudo-real-time analysis indicate that a simple nowcast combination based on these models provides faster estimates of TIO and GDP, without increasing substantially the revision error.

中文翻译：

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临近预报芬兰的实际经济活动：一种机器学习方法

我们基于微观数据开发了一个临近预报框架，以提供对芬兰每月实际经济活动指标，产出趋势指标（TIO）和季度GDP的更快估计。我们使用公司级的营业额（这是参考月结束后不久提供的）和实时流量数据来形成我们的一组预测指标。我们依靠从一系列能够处理高维信息集的统计模型和机器学习技术获得的临近预报的组合。我们的伪实时分析结果表明，基于这些模型的简单临近组合可以更快地估算TIO和GDP，而不会显着增加修订误差。