C - Mathematical and Quantitative Methods
-
-
Occasionally Binding Constraints in Large Models: A Review of Solution Methods
Solving macroeconomic models is difficult. One challenge is the occasionally binding constraint of the zero lower bound on nominal interest rates. This paper reviews various ways to solve models that include this feature. -
A Generalized Endogenous Grid Method for Default Risk Models
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. -
Cash and COVID-19: The Effects of Lifting Containment Measures on Cash Demand and Use
Using Bank Note Distribution System data on the demand for cash up to September 2020, we find that demand was strong. This is true even though cash use for payments declined early in the pandemic. When mobility restrictions and lockdown measures were eased, cash use for payments increased sharply but remained less popular than electronic methods of payment. -
Estimating Policy Functions in Payments Systems Using Reinforcement Learning
We demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information. -
(Optimal) Monetary Policy with and without Debt
How should policy be designed at high debt levels, when fiscal authorities have little room to adjust taxes? Assigning the monetary authority a role in achieving debt sustainability makes it less effective in stabilizing inflation and output. -
Networking the Yield Curve: Implications for Monetary Policy
We study how different monetary policies affect the yield curve and interact. Our study highlights the importance of the spillover structure across the yield curve for policy-making. -
Chinese Monetary Policy and Text Analytics: Connecting Words and Deeds
What are the main drivers behind the monetary policy reaction function of the People’s Bank of China? -
Qualitative Field Research in Monetary Policy Making
Central banks conduct research involving in-depth interviews with external parties—but little is known about how this information affects monetary policy. We address this gap by analyzing open-ended interviews with senior central bank economic and policy staff who work closely with policy decision-makers. -
Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19
We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.