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Natural disasters can create important challenges for monetary policy in resource-rich small open economies. Using a DSGE model calibrated to Canada, we show that most disasters operate as adverse supply shocks, lowering output and raising inflation, thereby creating a trade-off for monetary policy.

This paper develops Monte Carlo likelihood-ratio tests for determining the number of regimes in Markov switching models. Unlike most existing procedures, which focus on testing one versus two regimes, the proposed methods allow testing an arbitrary number of regimes. They are valid in finite samples, robust to identification problems, and applicable to nonstationary, multivariate, and Markov switching GARCH models.

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.

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.

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.

We study how participants in the Lynx payment system use the net send limit (NSL) tool to control their intraday payment outflow levels. Our results show that participants typically adopt a “set it and forget it” approach to scheduling NSLs and sometimes have distinct intraday NSL adjustment behaviours.

We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions.

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.

Extreme weather events contribute to increased volatility in both economic activity and prices, interfering with the assessment of the true underlying trends of the economy. With this in mind, we conduct a timely assessment of the impact of natural disasters on Canadian gross domestic product (GDP).