CANVAS: A Canadian Behavioral Agent-Based Model Staff Working Paper 2022-51 Cars Hommes, Mario He, Sebastian Poledna, Melissa Siqueira, Yang Zhang The Bank of Canada’s current suite of models faces challenges in addressing network effects that integrate household and firm-level heterogeneity and their behaviours. We develop CANVAS, a Canadian behavioural agent-based model to contribute to the Bank’s next-generation modelling effort. CANVAS improves forecasting performance and expands capacity for model-based scenario analysis. Content Type(s): Staff research, Staff working papers Research Topic(s): Central bank research, Econometric and statistical methods, Economic models, Firm dynamics, Inflation and prices JEL Code(s): C, D, D2, D22, D8, D83, E, E1, E17
Monetary Policy, Credit Constraints and SME Employment Staff Working Paper 2022-49 Julien Champagne, Émilien Gouin-Bonenfant We revisit an old question: how do financial constraints affect the transmission of monetary policy to the real economy? To answer this question, we propose a simple empirical strategy that combines firm-level employment and balance sheet data, identified monetary policy shocks and survey data on financing activities. Content Type(s): Staff research, Staff working papers Research Topic(s): Credit and credit aggregates, Econometric and statistical methods, Firm dynamics, Labour markets, Monetary policy JEL Code(s): E, E2, E3, E4, E43, E5, E52, G, G3
Core inflation over the COVID-19 pandemic Staff Analytical Note 2022-17 Mikael Khan, Elyse Sullivan We assess the usefulness of various measures of core inflation over the COVID-19 pandemic. We find that Cpi-trim and CPI-median provided the best signal of underlying inflation. The favourable performance of these measures stems from their lack of reliance on historical experience, an especially valuable feature in unprecedented times. Content Type(s): Staff research, Staff analytical notes Research Topic(s): Econometric and statistical methods, Inflation and prices, Monetary policy JEL Code(s): C, C1, C18, E, E3, E31
Private Digital Cryptoassets as Investment? Bitcoin Ownership and Use in Canada, 2016-2021 Staff Working Paper 2022-44 Daniela Balutel, Walter Engert, Christopher Henry, Kim Huynh, Marcel Voia We report on the dynamics of Bitcoin awareness and ownership from 2016 to 2021, using the Bank of Canada's Bitcoin Omnibus Surveys (BTCOS). Our analysis also helps understand Bitcoin owners who adopted during the COVID-19 and how they differ from long-term owners. Content Type(s): Staff research, Staff working papers Research Topic(s): Bank notes, Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C1, C12, E, E4, O, O5, O51
Examining recent revisions to CPI-common Staff Analytical Note 2022-15 Elyse Sullivan Unusually large revisions to CPI-common in recent months stem from increased common movements across consumer price index components amid broad inflationary pressures. With recent revisions, CPI-common is more closely aligned with the Bank of Canada’s other two preferred measures of core inflation. However, caution is necessary when interpreting real-time estimates of CPI-common in the current environment. Content Type(s): Staff research, Staff analytical notes Research Topic(s): Econometric and statistical methods, Inflation and prices JEL Code(s): C, C1, C13, C18, E, E3, E31
Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models Staff Discussion Paper 2022-19 James Younker This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. Computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Econometric and statistical methods JEL Code(s): C, C0, C01, C02, C1, C13, C5, C50, C51, C52, C53
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. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Monetary policy and uncertainty JEL Code(s): C, C5, C51, C55, E, E3, E32, E4, E44
Cash, COVID-19 and the Prospects for a Canadian Digital Dollar Staff Discussion Paper 2022-17 Walter Engert, Kim Huynh We provide an analysis of cash trends in Canada before and during the COVID-19 pandemic. We also consider the potential two scenarios for issuance of a central bank digital currency in Canada: the emergence of a cashless society or the widespread use of an alternative digital currency in Canada. Finally, we discuss the Canadian experience in maintaining cash as an efficient and accessible method of payment and store of value. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Bank notes, Central bank research, Coronavirus disease (COVID-19), Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C1, C12, C9, E, E4, O, O5, O54
Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models Staff Working Paper 2022-31 Xiangjin Shen, Iskander Karibzhanov, Hiroki Tsurumi, Shiliang Li 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. Content Type(s): Staff research, Staff working papers Research Topic(s): Credit risk management, Econometric and statistical methods JEL Code(s): C, C1, C14, C3, C35, C5, C51, C6, C63, D, D1
Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning Staff Working Paper 2022-29 Vladimir Skavysh, Sofia Priazhkina, Diego Guala, Thomas Bromley 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. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Central bank research, Econometric and statistical methods, Economic models, Financial stability JEL Code(s): C, C1, C15, C6, C61, C63, C68, C7, E, E1, E13, G, G1, G17, G2, G21