Abstract
The literature shows that the nowcasting models generally use structured data such as real, financial and survey indicators. Recent research has focused on finding the way how to use the unstructured data in the nowcasting models. The search items such as sentiments or emotions were gathered from internet platforms and used as unstructured data. In this study, it is analysed how the ECB presidents' speeches are included in the nowcasting model and to what degree they affect the quarterly gross domestic product (GDP) of Germany. First, ECB presidents' speeches are analysed to obtain the emotion indicators with assistance of the newly harmonised complex dictionary. These emotion indicators are next added to the unbalanced and mixed frequency data and the nowcasting model estimation for GDP is performed with these data using the expectation-maximisation algorithm in the dynamic factor model representation. Moreover, the news analysis is performed to show how the revisions in the real-time data, including emotion indicators, affect the nowcasts for the current and next quarter GDPs. Finally, a forecast scenario is performed to demonstrate the effects of emotion indicators in the nowcasting model of GDP which shows a slowdown for the last two years. In conclusion, it is suggested that ECB presidents' speeches may increase the performance of nowcasting models for the German GDP.
Highlights
Gross domestic product is the single most important economic indicator which shows the general economic situation of the given country
The second model is the model (Speech Model V-1) in which the emotion indicators group is included as a single factor to the nowcasting model
6 Conclusions In this study, emotion analysis is performed for the speeches of ECB presidents from 1997 to the end of 2019, and the indicators obtained from the emotion analysis are used as an additional input to the nowcasting model of the quarterly German gross domestic product (GDP)
Summary
Gross domestic product is the single most important economic indicator which shows the general economic situation of the given country. Varian and Choi (2009), McLaren and Shanbhogue (2011), Fondeur and Karamé (2013), Francesco and Marcucci (2017) and Bortoli and Combes (2015) and Baker et al (2016) used unstructured data in nowcasting models. There are very few studies that try to analyse nowcasting models by including sentiments or emotions It has been becoming popular in the literature to analyse emotions and associate them with economic indicators. The study by Kaminski and Gloor (2014) is another visionary paper on this topic in terms of analysing emotions rather than sentiments They examined the micro-blog data to obtain several emotion indicators and analysed the effect of them on crypto-currencies. Studies by Bollen et al (2010), Coviello (2014), Si et al (2013) and Zhang et al (2011) may be given as examples of associating economic nowcasting with emotion indicators obtained from micro-blog platforms
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