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Macroeconomic Forecasting with the help of Bayesian Adaptive Group LASSO MIDAS Model using Text Data from News Sources

Student: Rifat Eniliev

Supervisor: Oxana A. Malakhovskaya

Faculty: Faculty of Economic Sciences

Educational Programme: Applied Economics (Master)

Year of Graduation: 2020

This paper attempts to use text -based data from the news source Wall Street Journal article obtained using a thematic modeling approach such as Latent Dirichlet Allocation (LDA) for macroeconomic forecasting purposes in the presence of different frequency data , such as quarterly and monthly, as well as a small number of observations and a large number of potential regressors. The paper shows that text-based data from news sources actually improves the quality of forecasts in accordance with the root-of-mean-square-error quality metric, but this improvement is significant only on the horizon of more than one quarter.

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