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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.

We present the R package MSTest, which implements hypothesis testing procedures to determine the number of regimes in Markov switching models. The package provides several testing frameworks, including Monte Carlo likelihood ratio tests, moment-based tests, parameter stability tests, and classical likelihood ratio procedures.

Can monetary policy influence the neutral real interest rate (r-star)? Using a new statistical model, we show that interest rate hikes tend to lower r-star and long-run growth, but that monetary policy explains only a small share of the long-run decline in r-star.

We use a randomization of question placement in surveys to estimate the causal effect on financial literacy results. We find that financial literacy questions placed at the end of a survey lead to a drop in financial literacy of 5%–15%. This research suggests a measure of financial literacy adapted for survey length.

This paper quantifies the contribution of sector-specific supply and demand shocks to personal consumption expenditure (PCE) inflation. It derives identification restrictions that are consistent with a large class of dynamic stochastic general equilibrium models with production networks.

Inflation expectations are important to monetary policy decision-makers. Using survey evidence, we examine how firms and consumers react to their inflation expectations during the post-pandemic period of high inflation.

This note presents PULSE, a new measure of underlying inflation in Canada based on a dynamic factor model estimated on disaggregated inflation data. PULSE captures the persistent component of inflation and decomposes it into broad-based and sector-specific inflationary pressures.

We analyze Canadian data from the Bank of Canada’s Business Leaders’ Pulse, examining firms’ sales growth expectations. We find that expected growth predicts outcomes, uncertainty influences forecast errors and revisions, and firms with weak past performance anticipate and experience weaker future growth. These results highlight the survey’s value for understanding business expectations.

Can artificial intelligence (AI) think and act like a cash manager? In this paper we explore how generative AI agents can help manage liquidity, prioritize payments and optimize efficiency in real-time gross settlement systems.

I study the links between Canadian banks and non-bank financial intermediaries (NBFIs) by observing co-movements in stock prices. Perceived interconnections increased before the COVID-19 pandemic but have since stabilized, with the strongest ties seen between large banks and NBFIs. The secured credit line extended to Home Trust, a non-bank mortgage lender that experienced severe funding stress in 2017, significantly reduced banks' risk exposure to NBFIs during this episode.