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We study how the transmission of monetary policy innovations is affected by the endogenous response of the central bank to macroeconomic aggregates in a two-agent New Keynesian model. We focus on how the stance of monetary policy and the fraction of savers in the economy affect transmission.

I develop a method for Bayesian estimation of globally solved, non-linear macroeconomic models. The method uses a mixture density network to approximate the initial state distribution. The mixture density network results in more reliable posterior inference compared with the case when the initial states are set to their steady-state values.

We develop a new analytical framework to understand the system-wide implications of climate transition risk. When applying this framework to Canadian data, we find that interconnections within the financial sector could amplify the direct effects of climate transition risk on financial entities.

Our study aims to gain insight on financial stability and climate transition risk. We develop a methodological framework that captures the direct effects of a stressful climate transition shock as well as the indirect—or systemic—implications of these direct effects. We apply this framework using data from the Canadian financial system.

This paper proposes a unique approach to simulate intraday transactions in the Canadian retail payments batch system when such transactions are unobtainable. The simulation procedure has potential for helping with data-deficient problems where only high-level aggregate information is available.

We develop an algorithm and run it on a hybrid quantum annealing solver to find an ordering of payments that reduces the amount of system liquidity necessary without substantially increasing payment delays.

We use graphic processing unit computing to compare Bayesian and sample theory semiparametric binary response models. Our findings show that optimal bandwidth does not outperform regular bandwidth in binary semiparametric models.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

Our stress-testing tool considers banks under stress that can strategically manage their balance sheets. Using confidential Canadian supervisory data, we assess whether bank behaviour to maximize shareholder value can amplify a hypothetical stress scenario.

Models with default options are hard to solve. We propose an extension of the endogenous grid method that solves default risk models more efficiently and accurately.