The Effect of Oil Price Shocks on Asset Markets: Evidence from Oil Inventory News Staff working paper 2020-8 Ron Alquist, Reinhard Ellwanger, Jianjian Jin We quantify the reaction of U.S. equity, bond futures, and exchange rate returns to oil price shocks driven by oil inventory news. Content Type(s): Staff research, Staff working papers JEL Code(s): D, D8, D83, E, E4, E44, G, G1, G14, G15, Q, Q4, Q41, Q43 Research Theme(s): Financial markets and funds management, International markets and currencies, Market functioning, Monetary policy, Inflation dynamics and pressures
Non-competing Data Intermediaries Staff working paper 2020-28 Shota Ichihashi I study a model of competing data intermediaries (e.g., online platforms and data brokers) that collect personal data from consumers and sell it to downstream firms. Content Type(s): Staff research, Staff working papers JEL Code(s): D, D4, D42, D43, D8, D80, L, L1, L12 Research Theme(s): Financial markets and funds management, Market structure, Models and tools, Economic models, Money and payments, Digital assets and fintech
How Do Agents Form Macroeconomic Expectations? Evidence from Inflation Uncertainty Staff working paper 2024-5 Tao Wang The uncertainty regarding inflation that is observed in density forecasts of households and professionals helps macroeconomists understand the formation mechanism of inflation expectations. Shocks to inflation take time to be perceived by all agents in the economy, and such rigidity is lower in a high-inflation environment. Content Type(s): Staff research, Staff working papers JEL Code(s): D, D8, D84, E, E3, E31, E7, E71 Research Theme(s): Models and tools, Economic models, Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission
High-Frequency Trading and Institutional Trading Costs Staff working paper 2018-8 Marie Chen, Corey Garriott Using data on Canadian bond futures, we examine how high-frequency traders (HFTs) interact with institutions building large positions. In contrast to recent findings, we find HFTs in the data act as small-sized liquidity suppliers, and we reject the hypothesis that they engage in back running, a predatory trading strategy. Content Type(s): Staff research, Staff working papers JEL Code(s): G, G1, G14, G2, G20, L, L1, L10 Research Theme(s): Financial markets and funds management, Market functioning, Market structure
November 17, 1999 Monetary Policy Report – November 1999 Since the May Report, the international economic environment has continued to improve. Economic activity abroad grew faster than expected, while inflation in the major economies remained subdued. Content Type(s): Publications, Monetary Policy Report
Lending Standards, Productivity and Credit Crunches Staff working paper 2019-25 Jonathan Swarbrick We propose a macroeconomic model in which adverse selection in investment drives the amplification of macroeconomic fluctuations, in line with prominent roles played by the credit crunch and collapse of the asset-backed security market in the financial crisis. Content Type(s): Staff research, Staff working papers JEL Code(s): E, E2, E22, E3, E32, E4, E44, G, G0, G01 Research Theme(s): Financial system, Financial stability and systemic risk, Household and business credit, Models and tools, Economic models, Monetary policy, Real economy and forecasting
Housing and Tax-Deferred Retirement Accounts Staff working paper 2016-24 Anson T. Y. Ho, Jie Zhou Assets in tax-deferred retirement accounts (TDA) and housing are two major components of household portfolios. In this paper, we develop a life-cycle model to examine the interaction between households’ use of TDA and their housing decisions. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C6, C61, D, D1, D14, D9, D91, E, E2, E21, H, H2, H24, R, R2, R21 Research Theme(s): Financial system, Household and business credit, Models and tools, Economic models, Monetary policy, Real economy and forecasting
Machine learning for economics research: when, what and how Staff analytical note 2023-16 Ajit Desai This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used. Content Type(s): Staff research, Staff analytical notes JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
Markov‐Switching Three‐Pass Regression Filter Staff working paper 2017-13 Pierre Guérin, Danilo Leiva-Leon, Massimiliano Marcellino We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C2, C22, C23, C5, C53 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Real economy and forecasting
December 8, 2006 Perspectives on Productivity and Potential Output Growth: A Summary of the Joint Banque de France/Bank of Canada Workshop, 24–25 April 2006 Bank of Canada Review - Winter 2006-2007 Gilbert Cette, Donald Coletti A nation's productivity is the prime determinant of its real incomes and standard of living, as well as being a major determinant of its potential output. In the short run, deviations of actual output from potential output are a useful indicator of inflationary pressures. This article is a short summary of the proceedings of the workshop, which focus on productivity and potential output growth among industrialized countries. The research is organized under three main themes: estimating potential growth; productivity and growth; and institutions, policies, and growth. Content Type(s): Publications, Bank of Canada Review articles