Regional and Sectoral News-Based Indicators for Macroeconomic Forecasting
This paper combines dictionary-based methods and topic models to extract timely economic and financial signals at the sectoral (or 6-digit NAICS), provincial, and national levels from Canadian newspaper text and shows that such information can materially improve forecasts of macroeconomic variables, including GDP, inflation, housing prices, and unemployment. We use an advanced machine learning method to isolate information about future, current, and past sentiments. Such indices are extracted from approximately 2 million articles from major Canadian newspapers, including the National Post, Calgary Herald, Edmonton Journal, Montreal Gazette, Ottawa Citizen, Regina Leader-Post, The Globe and Mail, and Vancouver Sun.
How Media Narratives Influence Canadian Regional Housing Markets
Housing price prediction is a big challenge. The 2008 Global Financial Crisis (GFC) showed that even the most sophisticated traditional macro-financial models failed to foresee the crisis. In this paper, we investigate whether information from Canadian local newspaper articles about housing market narratives could improve local housing price predictions. We build separate future and past topic indexes to capture prior and posterior media narratives about the housing market. We use the mixed-frequency machine learning approach to generate a sequence of nowcasts/forecasts of monthly housing prices based on a vast local newspapers corpus related to the housing market. The predictions are based on linear models estimated via the LASSO and elastic net, nonlinear models based on artificial neural networks, and ensembles of linear and nonlinear models. The results indicate that news data contain valuable information about the housing market's direction.
Power Blackout ‘Pandemic' and Social Media Voice, with J., Agossa
The energy crisis in South Africa has become a major concern for governments, businesses, and consumers. While conventional survey methods to gauge public opinion on power blackouts are costly and time-consuming, Twitter has emerged as a useful tool for collecting data on the crisis, providing a more efficient and cost-effective way to gauge public sentiment through tweets. This study explores the use of Twitter to assess public sentiment on the energy crisis in South Africa, analyzing all tweets related to the issue from January 2010 to February 2023. By doing so, the study identified significant variations in sentiment across different cities and provinces, highlighting Twitter's value in understanding public sentiment and gaining insights into the issue. Furthermore, the study used machine learning techniques, such as Latent Dirichlet Allocation (LDA), to identify key topics discussed in the data, which could inform policy decisions to address the country's energy crisis. Additionally, the study found that the energy crisis has increased people's interest in renewable energy. Finally, the dynamic responses of macro variables to the identified energy crisis sentiment are consistent with the theoretical consensus. Overall, this study demonstrates the value of using Twitter as a tool for monitoring public sentiment and understanding the energy crisis in South Africa, providing a more cost-effective and efficient alternative to traditional survey methods.
"Narratives are major vectors of rapid change in culture, in zeitgeist, and in economic behavior."
—Robert Shiller (2019).