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• W4384698201 abstract "Chapter 1 presents a study using machine learning algorithms to forecast Canadian monthly real GDP growth using Google Trends (GT) data and official data, such as employment, before the official release of GDP data. Nonlinear tree-based ensemble algorithms like eXtreme Gradient Boosting (XGBoost), CatBoost, Random Forest (RF), and Microsoft's Light Gradient Boosting Machine (LightGBM), as well as the Support Vector Machine (SVM) algorithm are used for forecasting. The results show that CatBoost performs best for short-horizon forecasts, while SVM is effective for long-horizon forecasts. Chapter 2 introduces a natural language processing (NLP) based approach to measuring economic policy uncertainty (EPU) induced by the COVID-19 pandemic in Canada and the US. A newspaper-based EPU index is developed using several algorithms, including the rapid automatic keyword extraction algorithm (RAKE), RoBERTa and Sentence-BERT algorithms, PyLucene search engine, and GrapeNLP local grammar engine. The EPU-NLP index is compared to an index based on a strictly Boolean method, and it is found that the former is more effective in capturing COVID-19-related uncertainty. The EPU-NLP index generates larger declines in key macroeconomic variables than the EPU-Boolean index when subjected to a one standard deviation economic policy uncertainty shock using a structural VAR approach. Chapter 3 focuses on a unit-root problem that arises in small-open-economy RBC models. The closing devices proposed by Schmitt-Grohé and Uribe to induce stationarity in such models are analyzed. It is found that Seoane's analysis of the business cycle characteristics of Argentina, which identified a unit-root problem in the form of extreme persistence in consumption, output, and the trade-balance-to-output ratio, was affected by the values assigned to various parameters and his choice of a pruning method. The unit-root problem and inordinately large second moments are addressed by varying the values of the risk aversion parameter and the wage elasticity of labor supply, using various third-order perturbation methods, and applying third-order perturbation without pruning but with revised parameter values. Finally, the results show that the closing devices effectively generate greater consumption volatility than output and a counter-cyclical relationship between the trade balance and GDP." @default.
• W4384698201 created "2023-07-20" @default.
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• W4384698201 date "2023-07-18" @default.
• W4384698201 modified "2023-09-23" @default.
• W4384698201 title "Three Essays in Macroeconomics with a Focus on Forecasting GDP Growth with Machine Learning, Measuring Uncertainty with NLP, and Revisiting a Small Open Economy RBC Model" @default.
• W4384698201 doi "https://doi.org/10.22215/etd/2023-15577" @default.
• W4384698201 hasPublicationYear "2023" @default.
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