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415 Results

Borrow Now, Pay Even Later: A Quantitative Analysis of Student Debt Payment Plans

Staff working paper 2023-54 Michael Boutros, Nuno Clara, Francisco Gomes
We investigate alternative student debt contracts that defer payments and ease the burden of student loans on US households by preserving disposable income early in borrowers’ lives. Our model shows substantial welfare gains from these contracts relative to existing plans and gains similar to the Biden administration's proposals but with a significantly lower cost.

Identifying Nascent High-Growth Firms Using Machine Learning

Staff working paper 2023-53 Stéphanie Houle, Ryan Macdonald
Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.

A Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models

Staff discussion paper 2023-23 Donald Coletti
The fourth generation of Bank of Canada projection and policy analysis models seeks to improve our understanding of inflation dynamics, the supply side of the economy and the underlying risks faced by policy-makers coming from uncertainty about how the economy functions.

Labor Market Shocks and Monetary Policy

Staff working paper 2023-52 Serdar Birinci, Fatih Karahan, Yusuf Mercan, Kurt See
We develop a heterogeneous-agent New Keynesian model featuring a frictional labor market with on-the-job search to quantitatively study the positive and normative implications of employer-to-employer transitions for inflation.

Predicting Changes in Canadian Housing Markets with Machine Learning

Staff discussion paper 2023-21 Johan Brannlund, Helen Lao, Maureen MacIsaac, Jing Yang
We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant.

Digitalization: Definition and Measurement

Staff discussion paper 2023-20 Guyllaume Faucher, Stéphanie Houle
This paper provides an overview of digitalization and its economic implications. We assess the scope of digitalization in Canada as well as the challenges related to its measurement.

Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency

Staff discussion paper 2023-19 Chinara Azizova, Bruno Feunou, James Kyeong
This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022.

The Macroeconomic Effects of Debt Relief Policies During Recessions

Staff working paper 2023-48 Soyoung Lee
A large-scale reduction in mortgage principal can strengthen a recovery, support house prices and lower foreclosures. The nature of the intervention shapes its impact, which rests on how resources are redistributed across households. The availability of bankruptcy on unsecured debt changes the response to large-scale mortgage relief by reducing precautionary savings.

International Economic Sanctions and Third-Country Effects

Staff working paper 2023-46 Fabio Ghironi, Daisoon Kim, Galip Kemal Ozhan
We study the transmission and third-country effects of international sanctions. A sanctioned country’s losses are mitigated, and the sanctioning country’s losses amplified, if a third country does not join the sanctions, but the third country benefits from not joining.

Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis

Staff working paper 2023-45 Tony Chernis
I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.
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