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We construct a macroeconomic estimate of total AI production in the United States, combining inference and R&D/training activities with quality adjustments to account for algorithmic progress. We then develop a nascent framework for "AI GDP" that tracks the AI economy as a coherent whole, complementing traditional national accounts.

Amid heightened Canada–US trade tensions in 2025, financial markets showed signs that investors had greater difficulty anticipating near-term Bank of Canada interest rate decisions. We look at the Overnight Index Swap prices and intraday Government of Canada yields to identify the main driver of uncertainty around interest rate decisions.

How do central bank crisis interventions calm market fears? Using options data, we measure the perceived risk of large asset price drops across horizons from two weeks to ten years. Studying the Fed's response to the 2020 turmoil, we find asset purchases reduce short-term fears while interest rate actions shape long-term expectations.

We present the annual update of the Bank of Canada staff estimates for global potential output growth. These estimates served as key inputs to the analysis supporting the April 2026 Monetary Policy Report.

This paper examines how climate change affects income inequality across US states. Using a new climate-inequality VAR and a century of daily temperature data, it shows that shifts across the full temperature distribution—not just average warming—have diverse effects on within-state inequality.

The words central banks use to explain policy decisions matter. They can, in some cases, affect financial markets just like changes in policy interest rates do. For this reason, we built a tool to track the tone of the Bank of Canada’s policy communications and assess how tone affects market perceptions.

Mortgage debt represents over 70% of all Canadian household financial liabilities, and the performance of these debts is critical to the health of the financial system. We explore the relationships between mortgage arrears and key macroeconomic fundamentals such as labour market variables, interest rates, house prices and inflation. We then develop a framework to assess future household mortgage stress.

How can Surveys of Professional Forecasters (SPF) be used to improve inflation forecasts? By using US historical quarterly data on SPF forecasts, we provide better understanding of how we can use forecast disagreement to improve our own forecasts.

Statistical inference--both estimation and testing--for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student’s t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply “winsorization” to improve stability and finite-sample performance. Except for the degrees of-freedom parameter, closed-form expressions are available for all other parameters, extending Ahsan and Dufour (2019, 2021), thus eliminating the need for numerical optimization or initial values. We derive the estimators’ asymptotic distribution and show that, due to their analytical tractability, they support reliable, and even exact, simulation-based inference via Monte Carlo or bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models.

This paper introduces CLONE, a method that decomposes asset price movements into aggregate demand, productivity, inflation, and monetary policy news, using stocks, bonds, and inflation swaps. CLONE simplicity and forward-looking focus helps guide policymakers in determining the economic drivers behind asset price movements.