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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.

In markets with costly buyer entry, information transparency about prices draws in buyers, increasing demand-side competition and putting upward pressure on prices. We show that this buyer entry effect may dominate seller competition as emphasized by conventional wisdom and prices and markups may rise with information transparency.

The current monetary policy framework of the Fed intends to be more ’inclusive’ by running the economy hot for longer during expansions.

We quantify the effects of large-scale stock purchases by a central bank and compare these to bond purchases. We find that the central bank’s equity purchases would lower the risk and term premiums on stocks and long-term bonds, respectively, and thereby stimulate economic activity.

We provide a primer on the role of debt modelling in informing the sovereign debt issuance strategy and discuss how specific challenges faced by debt managers can influence model design decisions. These insights are supported by our experiences using the Canadian Debt Strategy Model to guide policy decisions.

We study the cross-section of equilibrium returns on safe assets using a tractable asset pricing model with a micro-founded demand for liquidity and multiple safe assets with heterogeneous transaction costs. We test the main predictions of our theory using a novel measure of relative (in)convenience yields in the US Treasury market.

This paper shows that survey expectations can be used to uncover how households subjectively think about inflation and unemployment dynamics jointly. The commonly documented "stagflation view", namely the households' tendency to associate inflation with a worse labor market, implies amplified impacts of supply shocks and dampened ones of demand shocks.

An alternative to the standard CES aggregator, based on non-homothetic household preferences, is proposed. Specifically, the elasticity of substitution between goods declines during periods of strong per-capita consumption and vice versa, giving firms an incentive to adjust their desired markup in response to the state of demand. Empirical evidence favouring a direct role for per-capita consumption demand in inflation determination for Canada is presented.

We produce forecasts for four risk scenarios to consider their usefulness for monitoring the Canadian economy. We find a high-oil-price scenario benefits the economy, a US recession induces a slowdown, a tight labor market leads to price increases, and a restrictive monetary policy scenario increases the unemployment rate while lowering the inflation rate.