Published Papers
Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth (with Ba Chu). Computational Economics, September 2022.
Using Natural Language Processing to Measure COVID19-Induced Economic Policy Uncertainty for Canada and US (with Ba Chu, Fanny S. Demers and Michel Demers). Book chapter in 'Contributions to Statistics', October 2022.
Predicting the COVID-19 Pandemic in Canada and the US (with Ba Chu). Economic Bulletin, Volume 40, Issue 3, September 2020.
Completed Working Papers
Forecasting Canadian GDP Growth with Machine Learning (with Ba Chu and Fanny S. Demers). Carleton Economics Working Papers (CEWP), May 2021.
Closing Open Economy Models for Emerging Countries (with Fanny S. Demers and Michel Demers).
Conference Presentations
Using Natural Language Processing to Measure COVID19-Induced Economic Policy Uncertainty for Canada and US (with Ba Chu, Fanny S. Demers and Michel Demers). 11th RCEA Money Macro and Finance Conference, July 2021.
Measuring COVID-19 Induced Economic Policy Uncertainty using Natural Language Processing for Canada and US (with Ba Chu, Fanny S. Demers and Michel Demers). 7th International Conference on Time Series and Forecasting (ITISE), July 2021, Gran Canaria, Spain.
Forecasting the COVID-19 Recession and Recovery for Canada and the US using Machine Learning and Deep Learning Methods (with Ba Chu, Fanny S. Demers). 7th International Conference on Time Series and Forecasting (ITISE), July 2021, Gran Canaria, Spain.
Current Research Projects
Forecasting the COVID-19 Recession and Recovery for Canada and the US using Machine Learning and Deep Learning Methods (with Ba Chu and Fanny S. Demers).
The Causal Impact of Policy Interest Rate on Canadian Inflation with Machine Learning (with Ba M. Chu, Fanny S. Demers, Nima Hejazi and Michel Demers).
What Triggers Stock Market Jumps? Using NLP (with Ba Chu, Fanny S. Demers, Asma Djaidri and Saad Hassan Khan).