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485 Results

Supply Drivers of US Inflation Since the COVID-19 Pandemic

Staff working paper 2023-19 Serdar Kabaca, Kerem Tuzcuoglu
This paper examines the contribution of several supply factors to US headline inflation since the start of the COVID-19 pandemic. We identify six supply shocks using a structural VAR model: labor supply, labor productivity, global supply chain, oil price, price mark-up and wage mark-up shocks.

Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19

Staff working paper 2021-2 James Chapman, Ajit Desai
We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.

Bank Runs, Portfolio Choice, and Liquidity Provision

Staff working paper 2019-37 Toni Ahnert, Mahmoud Elamin
After the financial crisis of 2007–09, many jurisdictions introduced new banking regulations to make banks more resilient and less likely to fail. These regulations included tighter limits for the quality and quantity of bank capital and introduced minimum standards for liquidity. But what was the impact of these changes?

Social Learning and Monetary Policy at the Effective Lower Bound

This research develops a model in which the economy is directly influenced by how pessimistic or optimistic economic agents are about the future. The agents may hold different views and update them as new economic data become available.

A Dynamic Factor Model for Nowcasting Canadian GDP Growth

Staff working paper 2017-2 Tony Chernis, Rodrigo Sekkel
This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data.

Are Long-Horizon Expectations (De-)Stabilizing? Theory and Experiments

Staff working paper 2019-27 George Evans, Cars Hommes, Isabelle Salle, Bruce McGough
Most models in finance assume that agents make trading plans over the infinite future. We consider instead that they are boundedly rational and may only form forecasts over a limited horizon.

Good Volatility, Bad Volatility and Option Pricing

Staff working paper 2017-52 Bruno Feunou, Cédric Okou
Advances in variance analysis permit the splitting of the total quadratic variation of a jump diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions, and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions.
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