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175 Results

Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks

Staff working paper 2025-10 Zhentong Lu, Kenichi Shimizu
We propose a novel approach to estimating consumer demand for differentiated products. We eliminate the need for instrumental variables by assuming demand shocks are sparse. Our empirical applications reveal strong evidence of sparsity in real-world datasets.

Canadian Bitcoin Ownership in 2023: Key Takeaways

Staff discussion paper 2025-4 Daniela Balutel, Marie-Hélène Felt, Doina Rusu
The Bitcoin Omnibus Survey is an important tool for monitoring Canadians’ awareness and ownership of bitcoin and other cryptoassets over time. In this paper, we present data highlights from the 2023 survey.

Estimating the inflation risk premium

Staff analytical note 2025-9 Bruno Feunou, Gitanjali Kumar
Is there a risk of de-anchoring of inflation expectations in the near term? We estimate the inflation risk premium using traditional asset pricing models to answer this question. The risk of de-anchoring is elevated compared with the period before the COVID-19 pandemic and is higher in the United States than in Canada.

Quantile VARs and Macroeconomic Risk Forecasting

Staff working paper 2025-4 Stéphane Surprenant
This paper provides an extensive evaluation of the performance of quantile vector autoregression (QVAR) to forecast macroeconomic risk. Generally, QVAR outperforms standard benchmark models. Moreover, QVAR and QVAR augmented with factors perform equally well. Both are adequate for modeling macroeconomic risks.

Differentiable, Filter Free Bayesian Estimation of DSGE Models Using Mixture Density Networks

Staff working paper 2025-3 Chris Naubert
I develop a method for Bayesian estimation of globally solved, non-linear macroeconomic models. The method uses a mixture density network to approximate the initial state distribution. The mixture density network results in more reliable posterior inference compared with the case when the initial states are set to their steady-state values.

CBDC in the Market for Payments at the Point of Sale: Equilibrium Impact and Incumbent Responses

Staff working paper 2024-52 Walter Engert, Oleksandr Shcherbakov, André Stenzel
We simulate introducing a central bank digital currency (CBDC) and consider consumer adoption, merchant acceptance and usage at the point of sale. Modest adoption frictions significantly inhibit CBDC market penetration along all three dimensions. Incumbent responses to restore pre-CBDC market shares are moderate to small and further reduce the impact of a CBDC.

Familiarity with Crypto and Financial Concepts: Cryptoasset Owners, Non-Owners, and Gender Differences

Measuring cryptoasset knowledge alongside financial knowledge enhances our understanding of individuals' decisions to purchase cryptoassets. This paper uses microdata from the Bank of Canada’s Bitcoin Omnibus Survey to examine gender differences and the interrelationship between crypto and financial knowledge through an empirical joint analysis.

The (Mis)Allocation of Corporate News

Staff working paper 2024-47 Xing Guo, Alistair Macaulay, Wenting Song
We study how the distribution of information supply by the news media affects the macroeconomy. We find that media coverage focuses particularly on the largest firms, and that firms’ equity financing and investment increase after media coverage. But these equity and investment responses are largest among small, rarely covered firms. Our quantitative studies highlight that the aggregate effects of media coverage depend crucially on how that coverage is allocated.

Seasonal Adjustment of Weekly Data

Staff discussion paper 2024-17 Jeffrey Mollins, Rachit Lumb
The industry standard for seasonally adjusting data, X-13ARIMA-SEATS, is not suitable for high-frequency data. We summarize and assess several of the most popular seasonal adjustment methods for weekly data given the increased availability and promise of non-traditional data at higher frequencies.

Decision Synthesis in Monetary Policy

Staff working paper 2024-30 Tony Chernis, Gary Koop, Emily Tallman, Mike West
We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes.
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