Hello. My name is Robert MacDonald. I am a PhD candidate in Economics at University of California, Irvine.

My research is in Econometrics. I design new methods for modeling time series data for use in macroeconomics and finance. I am passionate about building flexible and nonlinear techniques that can adapt to the deep complexity of economic and financial markets. I take a Bayesian approach, employing both classical and modern machine learning tools to uncover hidden relationships and make robust economic forecasts. You can learn more about my job market paper below.

Specification of FAVAR Models (Job Market Paper)

This paper proposes a novel methodology for determining the specification of factor-augmented vector autoregression (FAVAR) models. Without strong a priori beliefs about the set of possible models, the complexity of the problem renders traditional model selection techniques infeasible. By contrast, my proposed solution only requires the estimation of a single model. This makes the process easy to scale in both the cross-sectional and time series dimensions. An efficient optimization algorithm for model estimation is developed. Monte Carlo studies show the technique to be highly effective in small samples, even in the presence of a low signal-to-noise ratio and missing data. Applications to large datasets of monthly and quarterly U.S. macroeconomic variables identify observed factors not normally considered in the FAVAR literature. The methodology is then used to analyze the asset-pricing model of Fama and French (1993). I find that their constructed factors for firm size and book-to-market equity ratio are likely observed components, but excess market return is not.