Publication

  1. Food Security and COVID-19 Employment Shock in Nigeria: Any ex-ante Mitigating Effects of Past Remittances?, with Al-mouksit Akim and Jeffrey Kouton

    2. [ PDF ] Food Policy.

    • Presentations: SCSE Conference 2023, ICDE 2023: International Conference on Development Economics, CSAE Conference 2022:Economic Development in Africa, 2021 Africa Meeting of the Econometric Society, 2021 International Conference in Development Economics, GLAD 2021, Pan-African Scientific Research Council 2021, Quebec Social Sciences PhD Students Seminar 2021

Working Papers

  1. High-Frequency Inflation Expectations from Big Data: A Natural Language Approach [ PDF ]

    2. In this study, I leverage large language models (LLMs) in natural language processing to scrutinize a comprehensive dataset of more than 2 million newspaper articles and 40 million tweets across Canadian provinces. This method is employed to develop novel high-frequency and real-time indicators of consumer inflation expectations at both national and subnational levels. I first identify news articles and tweets related to inflation or prices. Additionally, I apply deep learning methods, particularly LLMs to extract information specifically related to future price dynamics. Then, I construct daily measures of text-based inflation expectations as the difference between the number of news articles or tweets about inflation and the number of news articles or tweets about deflation. The results indicate a high correlation between the resulting text-based inflation expectations indices with consumers’ survey-based inflation expectations and realized inflation. Subsequently, I use a mixed-frequency machine learning approach to generate nowcasts/forecasts of quarterly inflation expectations and actual inflation based on large sets of text indicators and Google Trends search volume data for inflation-related terms. The analysis demonstrates that news and social media data contain valuable information regarding inflation dynamics and my newly developed indicators effectively anticipate consumer inflation expectations and actual inflation. The paper further explores the application of Shapley additive explanations (SHAP) values to enhance the interpretability of complex, nonlinear models. The findings suggest that newspaper and social media data can serve as a timely source for market participants and policymakers to elicit beliefs on inflation or future price dynamics.

    • Presentations: MS & E Rising Stars Workshop 2024 (Stanford University), 2023 NBER-NSF Time Series Conference, poster session, Bank of England (BoE), 1st CIREQ Interdisciplinary Conference on Big Data and Artificial Intelligence (2023), SCSE Conference (2023), 3rd GREDI/CREATE/CIREQ PhD Student Research Workshop, UdeM Department of Mathematics and Statistics Brownbag Seminar (2023), HEC Montreal, Concordia University, UdeM Macroeconomic Brown Bag, 18th CIREQ PhD Students Conference (2023), Quebec Social Sciences PhD Students Seminar (2023)


  2. Can Media Narratives Predict House Price Movements?, with Christopher Rauh

    3. This paper investigates how the housing market, a major asset in household wealth, mirrors broader economic trends and we present a predictive model for housing price movements in Canada at both provincial and national levels. Our methodology unfolds in two distinct stages: initially, we process over 2 million newspaper articles through natural language processing techniques to extract media narratives, analyze sentiments, and sort articles according to their focus on past, present, or future events. We implement mixed-frequency machine learning methods to generate a sequence of predictions for quarterly housing prices out-of-sample. The predictions are based on linear models estimated via the LASSO, Ridge, and Elastic net, nonlinear models based on Random Forests, Extreme Gradient Boosting, Artificial Neural Networks, and ensembles of linear and nonlinear models, with ensembles providing the best forecasting performance. The results indicate that news data contain valuable information about the housing market's direction and that predictive performance improves over longer time horizons.

    • Presentations: International Association for Applied Econometrics (IAAE) Annual Conference*, 39th Annual Meeting of the Canadian Econometrics Study Group (CESG), Conference on Real-Time Data Analysis, Methods and Applications in Macroeconomics and Finance (2024), ECONDAT 2024 spring meeting: Economics with Nontraditional Data and Analytical Tools, Econometric Society Economics and AI+ML Conference (ESIF-AIML, 2024), BFI Macro-Finance Research Program Summer Session for Young Scholars, Econometric Society North American Summer Meeting (NASM, 2024), Quebec Social Sciences PhD Students Seminar (2023)


  3. Identification and Estimation of Common Factors in Group Factor Models

    4. This paper examines the comovement among factors extracted from two distinct large panels (or groups) of variables. I show that estimating factors introduces a bias in the estimated correlation between factors, which becomes negligible if the factors are estimated from panel data sets containing a large number of cross-sectional series. I 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. Additionally, I apply my modified wild bootstrap method to analyze the influence of institutional factors on economic growth, as examined by Deniz et al. (2018), and the degree of synchronization of business cycles in developed and emerging economies, as explored in Kose et al. (2013) and Aastveit et al. (2015).

    • Presentations: 17th International Conference on Computational and Financial Econometrics (Berlin, 2023), Annual Meetings of the Canadian Economics Association (CEA, 2022), Conference in Honor of Eric Renault 2022, SCSE Conference 2021, CIREQ Ph.D. Students’Conference 2021, Quebec Social Sciences PhD Students Seminar 2021


  4. Regional and Sectoral News-Based Indicators for Macroeconomic Forecasting

    5. This paper evaluates the informational content of sentiment extracted from news articles about the state of the economy. First, I 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 October 2022. These sentiment indices are constructed at the sectoral (or 6-digit NAICS), provincial, and national levels. Second, I document that the sentiment indices significantly correlate with contemporaneous key economic and financial variables such as GDP, inflation, housing prices, and unemployment. Third, I 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 nonlinear machine learning techniques, ensembles of linear and nonlinear models.

    • Presentations: Annual Meetings of the Canadian Economics Association (CEA, 2023), Annual Toronto Machine Learning Summit 2022, IVADO Digital October 2022, SCSE Conference 2022, Quebec Social Sciences PhD Students Seminar 2021


Other papers

  1. Economic Government Support and Lockdown-Compliance in Africa, with Al-mouksit Akim [SSRN]

Policy Papers

  1. Fiscal Vulnerabilities and the Role of Fiscal Policy in Commodity-Exporting Countries, with Christine Richaud, Sebastian Essl, Arthur Galego, and Samer Matta [PDF]


"...When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues. We must be careful in our choice of statistical methods and the algorithms used to implement them.", (James G. MacKinnon, JoE 2023)