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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.

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.

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.

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.

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.