We propose forward-looking indexes for inflation based on tweets and newspaper articles employing a supervised machine-learning approach. Using Brazilian data, we verify that the news-based indexes were able to anticipate long-term trends as well as capture short-term movements of the accumulated inflation over 3, 6, and 12 months ahead at various periods. Furthermore, the proposed indexes could improve inflation forecast performance. More specifically, for short horizons (3 and 6 months ahead), a bias correction model for the median of available survey-based expectations benefits from including news-based indexes. On the other hand, for longer-term inflation (12 months ahead), models incorporating a large number of predictors can also be improved by incorporating the indexes. Thus, considering indexes from social media and news sources can improve inflation forecasting. The intuition for the result is that it pays to consider a broader set of information than solely that resulting from survey-based expectations that account only for experts' opinions.
Comissão Organizadora
Anderson Odias da Silva
Claudia Yoshinaga
Ricardo D. Brito
Felipe Saraiva Iachan
Vinicius Augusto Brunassi Silva
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