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Macroeconomic Forecasting Using Social Media Data

ФИО студента: Shulyak Elena

Руководитель: Mariam Mamedli

Кампус/факультет: Faculty of Economic Sciences

Программа: Statistical Modelling and Actuarial Science (Master)

Год защиты: 2021

In this study, we collected news posts and comments for these posts from Russian social media “Vkontakte”. We preprocessed texts, distinguished news posts about the economy from all the posts on other topics with the GSDMM method. Then, with this data and text analysis methods, we built a set of economic sentiment indexes for Russia. To examine whether these indexes have the ability to describe sentiments of consumers and business, we compared them with the existing ones: Consumer Sentiment Index by Levada-Center, PMI Services, and PMI Manufacturing for Russia. Then we performed forecasts of macroeconomic variables with Machine Learning methods, namely Random Forest, Extremely Randomized Trees, Gradient Boosting, XGBoost, and the obtained indexes and some additional information from “Vkontakte” as the features. We compared mean squared errors of machine learning models with AR(1) errors, and we showed that ML models outperformed AR(1) in almost all the cases.

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