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17 Results

Household Risk Assessment Model

Technical report No. 106 Brian Peterson, Tom Roberts
Household debt can be an important source of vulnerability to the financial system. This technical report describes the Household Risk Assessment Model (HRAM) that has been developed at the Bank of Canada to stress test household balance sheets at the individual level.

Anonymous Credentials: Secret-Free and Quantum-Safe

Staff working paper 2023-50 Raza Ali Kazmi, Cyrus Minwalla
An anonymous credential mechanism is a set of protocols that allows users to obtain credentials from an organization and demonstrate ownership of these credentials without compromising users’ privacy. In this work, we construct the first secret-free and quantum-safe credential mechanism.

Generalized Autoregressive Gamma Processes

Staff working paper 2023-40 Bruno Feunou
We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does.

Fiscal Policy in the Age of COVID-19: Does It “Get in All of the Cracks”?

The COVID-19 pandemic has caused an atypical recession in which some sectors of the economy boomed and others collapsed. This required a unique fiscal policy reaction to both support firms and stimulate activity in sectors with slack. Was fiscal policy able to get where it was needed? Mostly, yes.

Energy Efficiency and Fluctuations in CO2 Emissions

Staff working paper 2021-47 Soojin Jo, Lilia Karnizova
Carbon dioxide emissions have been commonly modelled as rising and falling with total output. Yet many factors, such as energy-efficiency improvements and shifts to cleaner energy, can break this relationship. We evaluate these factors using US data and find that changes in energy efficiency of consumption goods explain a significant proportion of emissions fluctuations. This finding also implies that models that omit energy efficiency likely overestimate the trade-off between environmental protection and economic performance.

Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning

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.

Time Use and Macroeconomic Uncertainty

Staff working paper 2023-29 Matteo Cacciatore, Stefano Gnocchi, Daniela Hauser
We estimate the effects of economic uncertainty on time use and discuss its macroeconomic implications. We develop a model to demonstrate that substitution between market and non-market work provides an additional insurance margin to households, weakening precautionary savings and labour supply and lowering aggregate demand, ultimately amplifying the contractionary effects of uncertainty.

Producer Heterogeneity, Value-Added, and International Trade

Staff working paper 2016-54 Patrick Alexander
Standard new trade models depict producers as heterogeneous in total factor productivity. In this paper, I adapt the Eaton and Kortum (2002) model of international trade to incorporate tradable intermediate goods and producer heterogeneity in value-added productivity.

Impacts of interest rate hikes on the consumption of households with a mortgage

We assess how much the recent rate-hike cycle has and will affect mortgage borrowers' consumption through its impacts on mortgage payments. Our analysis provides insights into the effects of changes in monetary policy on the consumption of mortgage borrowers.

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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