Staff working papers

Staff working papers provide a forum for staff to publish work-in-progress research intended for journal publication.

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1320 result(s)

Sectoral Uncertainty

Staff Working Paper 2022-38 Efrem Castelnuovo, Kerem Tuzcuoglu, Luis Uzeda
We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a disaggregated industrial production series for the US economy. We identify unexpected changes in durable goods uncertainty as drivers of downturns, while unexpected hikes in non-durable goods uncertainty are expansionary.

Risk and State-Dependent Financial Frictions

Staff Working Paper 2022-37 Martin Harding, Rafael Wouters
Using a nonlinear New Keynesian model with a financial accelerator, we show that financial frictions generate large state-dependent amplification effects. Shocks propagate more strongly in periods of financial stress. We propose an endogenous regime-switching DSGE framework for efficient estimation and improved model fit.

Weather the Storms? Hurricanes, Technology and Oil Production

Do technological improvements mitigate the potential damages from extreme weather events? We show that hurricanes lower offshore oil production in the Gulf of Mexico and that stronger storms have larger impacts. Regulations enacted in 1980 that required improved offshore construction standards only modestly mitigated the production losses.

PayTech and the D(ata) N(etwork) A(ctivities) of BigTech Platforms

Staff Working Paper 2022-35 Jonathan Chiu, Thorsten Koeppl
Why do BigTech platforms introduce payment services? We explore this using a model in which a monopoly platform faces a trade-off between the costs associated with privacy concerns and the revenue from data services. We then analyze the feedback effects between data and payments.

How Do People View Price and Wage Inflation?

Staff Working Paper 2022-34 Monica Jain, Olena Kostyshyna, Xu Zhang
This paper examines household-level data from the Canadian Survey of Consumer Expectations (CSCE) to understand households’ expectations about price and wage inflation, how those expectations link to views about labour market conditions and the subsequent impact on households’ outlook for real spending growth.

A Horse Race of Monetary Policy Regimes: An Experimental Investigation

Staff Working Paper 2022-33 Olena Kostyshyna, Luba Petersen, Jing Yang
How should central banks design monetary policy in stable times and during recessions? We run a horse race between five monetary policy frameworks in an experimental laboratory to assess how well the different approaches can manage the public’s expectations and stabilize the economy.

Cyber Risk and Security Investment

Staff Working Paper 2022-32 Toni Ahnert, Michael Brolley, David Cimon, Ryan Riordan
We develop a principal-agent model of cyber-attacking with fee-paying clients who delegate security decisions to financial platforms. We derive testable implications about clients’ vulnerability to cyber attacks and about the fees charged.

Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models

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.

International Transmission of Quantitative Easing Policies: Evidence from Canada

Staff Working Paper 2022-30 Serdar Kabaca, Kerem Tuzcuoglu
This paper examines the cross-border spillovers from major economies’ quantitative easing (QE) policies to their trading partners. We concentrate on spillovers from the US to Canada during the zero lower bound period when QE policies were actively used.

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.
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