Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models. Overall, mixed frequency dynamic factor models and neural networks perform well, both in absolute terms and in relative terms with respect to a benchmark autoregressive model. The gains are larger during problematic times, such as the financial crisis and the recent Covid period.

中文翻译：

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小规模开放经济体中的 GDP 增长预测

临近预报，即预测当前的经济状况，是决策的关键要素，但由于其 GDP 的较高波动性，对于小型开放经济体来说更是如此。在本文中，我们以卢森堡为例回顾了所需的步骤。我们考虑了标准指标和替代指标，在几种临近预报方法中用作输入，包括各种因素和机器学习模型。总体而言，相对于基准自回归模型，混合频率动态因子模型和神经网络在绝对值和相对值上都表现良好。在金融危机和最近的 Covid 时期等问题时期，收益更大。