The overall goal of this thesis is to provide a deeper understanding of the role of dynamic stochastic general equilibrium (DSGE) models as foundations upon which empirical work is conducted. This is a very broad topic with a large existing literature. For this purpose, my dissertation focuses on applying the tools and rich structure of DSGE models to answer questions that have hitherto been studied only by using a reduced-form characterization. I have chosen to look at two specific macroeconomic questions of interest: the economic consequences of oil price shocks in Canada and the role of intangible capital (IC) in explaining cyclical dynamics of S&P500 earnings. Chapter 2 look at the economic consequences of oil price shocks in a structural vector autoregressions (VAR) framework. Chapter 3 builds on this by developing an open economy DSGE model to investigate the impact of oil price shocks on the aggregate Canadian economy and to quantify the relative contribution of U.S. and Canadian monetary policy in transmitting oil price shocks. Chapter 4 studies another interesting macroeconomic phenomenon: the excess volatility of aggregate profits. We embed intangible capital into an otherwise standard real business cycle (RBC) model to examine the role of intangible capital in driving cyclical dynamics of S&P500 earnings. A common feature of my papers is the application of Bayesian time series techniques to macroeconomic data to pursue new insights on "the impact of oil price shocks on economic activities", "the role of monetary policy in transmitting oil price shocks" in new open economic macroeconomics (NOEM) literature and "intangible capital and corporate earnings" in U.S. business cycle literature.

Doctor of Philosophy (PhD)

Thesis