Essays on Time Series Forecasting: Applications in Macroeconomics and Finance

In an era marked by significant economic upheavals such as the Great Recession and the COVID-19 pandemic, the need for reliable and timely forecasting tools has become increasingly urgent. In particular, the inflationary shock induced by the COVID-19 crisis and Russia’s invasion of Ukraine highlighted the challenges posed to anticipating in real-time the current state of the inflation process. Simultaneously, the pronounced volatility spikes and structural instabilities observed in bond markets during turbulent times underscored the need for more plausible density forecasts of the yield curve for strategic bond portfolio allocation and effective interest rate risk management. This dissertation examines these aspects of economic forecasting through three independent articles that integrate advanced econometric and machine learning methods with high-frequency data. These studies offer fresh insights into the efficacy of these methodologies in real-time forecasting and provide actionable insights for practitioners and policymakers, enhancing decision-making in turbulent economic environments.

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