Risk Amplification Macro Model (RAMM) Technical Report No. 123 Kerem Tuzcuoglu The Risk Amplification Macro Model (RAMM) is a new nonlinear two-country dynamic model that captures rare but severe adverse shocks. The RAMM can be used to assess the financial stability implications of both domestic and foreign-originated risk scenarios. Content Type(s): Staff research, Technical reports Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Financial stability, Monetary policy transmission JEL Code(s): C, C5, C51, E, E3, E37, E4, E44, F, F4, F44
Macroeconomic Disasters and Consumption Smoothing: International Evidence from Historical Data Staff Working Paper 2023-4 Lorenzo Pozzi, Barbara Sadaba Does consumption smoothing fundamentally decrease during macroeconomic disasters? This paper uses a large historical dataset (1870–2016) for 16 industrial economies to show that during macroeconomic disasters (e.g., wars, pandemics, depressions) aggregate consumption and income are significantly less decoupled than during normal times. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Coronavirus disease (COVID-19), Econometric and statistical methods JEL Code(s): C, C2, C23, E, E2, E21
Are Temporary Oil Supply Shocks Real? Staff Working Paper 2022-52 Johan Brannlund, Geoffrey R. Dunbar, Reinhard Ellwanger Hurricanes disrupt oil production in the Gulf of Mexico because producers shut in oil platforms to safeguard lives and prevent damage. We examine the effects of these temporary oil supply shocks on real economic activity in the United States. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Inflation and prices JEL Code(s): E, E3, E31, E32, Q, Q3, Q31, Q4, Q41, Q43
Understanding Post-COVID Inflation Dynamics Staff Working Paper 2022-50 Martin Harding, Jesper Lindé, Mathias Trabandt We propose a macroeconomic model with a nonlinear Phillips curve that has a flat slope when inflationary pressures are subdued and steepens when inflationary pressures are elevated. Our model can generate more sizable inflation surges due to cost-push and demand shocks than a standard linearized model when inflation is high. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Central bank research, Coronavirus disease (COVID-19), Economic models, Inflation and prices, Inflation: costs and benefits, Monetary policy, Monetary policy implementation JEL Code(s): E, E3, E30, E31, E32, E37, E4, E44, E5, E52
Canada’s Beveridge curve and the outlook for the labour market Staff Analytical Note 2022-18 Alexander Lam Canada’s labour market is tight but beginning to ease. Unemployment will likely rise in turn, but the economy can avoid a recessionary surge given current conditions. Higher unemployment would nonetheless be material, especially for those directly impacted. Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Central bank research, Labour markets, Recent economic and financial developments JEL Code(s): E, E3, E32, J, J2, J20, J6, J63, J64
Behavioral Learning Equilibria in New Keynesian Models Staff Working Paper 2022-42 Cars Hommes, Kostas Mavromatis, Tolga Özden, Mei Zhu We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Economic models, Inflation and prices, Monetary policy JEL Code(s): C, C1, C11, D, D8, D83, D84, E, E3, E6, E62
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 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
Weather the Storms? Hurricanes, Technology and Oil Production Staff Working Paper 2022-36 Johan Brannlund, Geoffrey R. Dunbar, Reinhard Ellwanger, Matthew Krutkiewicz 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. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Climate change, Potential output JEL Code(s): C, C2, C22, C23, Q, Q4, Q40, Q48, Q5, Q54
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. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, International topics, Monetary policy transmission JEL Code(s): E, E5, E52, F, F4, F41, F44
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 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