- Identification and Estimation of Common Factors in Panel Data
- Regional and Sectoral News-Based Indicators for Macroeconomic Forecasting
- How Media Narratives Influence Canadian Regional Housing Markets
This paper examines the estimation of relations among factors extracted from different panel data sets. Two interesting applications are the factors contributing to economic growth and the synchronicity of business cycles. We show that the estimation of factors induces a bias in the estimated correlation between factors which disappears if the factors are estimated from panel data sets that contain a large number of cross-sectional series. We show that a modified version of the wild bootstrap algorithm proposed by Gonçalves and Perron (2014) can correct the bias and provide reliable inference on the correlation of interest. Finally, we apply our modified bootstrap method to the contributions of institutional factors to economic growth analyzed in Deniz et al. (2018) and to the degree of synchronization of business cycles among developed countries and emerging economies in Kose et al. (2013) and Aastveit et al. (2015).
• Presentations: CEA 2022, Conference in Honor of Eric Renault 2022, SCSE 2021, CIREQ Ph.D. Students’Conference 2021, Quebec PhD WESF 2021
This paper evaluates the informational content of sentiment extracted from news articles about the state of the economy. First, we apply deep learning and lexical-based techniques to construct a new high-frequency measure of sentiment indices embodied in a vast news corpus covering economic and financial articles in Canada from January 1977 to March 2022. These sentiment indices are constructed at the sectoral (or 6-digit NAICS), provincial, and national levels. Second, we document that the sentiment indices significantly correlate with contemporaneous key economic and financial variables such as GDP, inflation, housing prices, and unemployment. Third, we use an advanced machine learning method to isolate information about future, current, and past sentiments. Finally, this paper provides novel evidence of how news sentiment tracks current and future economic and financial conditions and significantly enhances predictive power in forecasting models using shrinkage methods and non-linear machine learning techniques.
• Presentations: Annual Toronto Machine Learning Summit (TMLS) 2022, IVADO Digital October 2022, SCSE 2022, Quebec PhD WESF 2021
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.
• Presentations: Quebec PhD WESF 2021
Work in Progress
- Deep Dynamic Factor Models in a Data‐Rich Environment
- High-Frequency Inflation Expectations from Big Data: A Natural Language Approach
- Power Blackout ‘Pandemic' and Social Media Voice, with J., Agossa
- Food Security and COVID-19 Employment Shock in Nigeria: Any ex-ante Mitigating Effects of Past Remittances?, with A., Akim and J., Kouton [SSRN][Covid Economics - Issue 78]
- Economic Government Support and Lockdown-Compliance in Africa, with A., Akim [SSRN]
- More Than Words: A Textual Analysis of MEFP, with J., Andritzky, and H., Hesse
- Network Effects and IMF Program Review Teams, with J., Andritzky, and H., Hesse
- Fiscal Vulnerabilities and the Role of Fiscal Policy in Commodity-Exporting Countries, with C., Richaud, S., Essl, A., Mendes, and S., Matta [pdf]
- Regional Debt Market in the Waemu: Curse Or Blessing?, with B., Loko, and C., Richaud