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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.

The Canadian Financial Monitor (CFM) survey uses non-probability sampling for data collection, so selection bias is likely. We outline methods for obtaining survey weights and discuss the conditions necessary for these weights to eliminate selection bias. We obtain calibration weights for the 2018 and 2019 online CFM samples.

This note presents a composite indicator of Canadian financial system vulnerabilities—the Vulnerabilities Barometer. It aims to complement the Bank of Canada’s vulnerabilities assessment by adding a quantitative and synthesized perspective to the more granular (distributional) analysis presented in the Financial System Review.

Economic activities typically involve coordination among a large number of agents. These agents have to anticipate what other agents think before making their own decisions.

The Canadian economy is expected to grow by 1.6 per cent in 2013, 2.3 per cent in 2014 and 2.6 per cent in 2015, and to reach full production capacity around the end of 2015.

The uncertainty around future changes to the Federal Reserve target rate varies over time. In our results, the main driver of uncertainty is a “path” factor signaling information about future policy actions, which is filtered from federal funds futures data.

This paper shows that real effective exchange rate (REER) regressions, the standard approach for estimating the response of aggregate exports to exchange rate changes, imply biased estimates of the underlying elasticities. We provide a new aggregate regression specification that is consistent with bilateral trade flows micro-founded by the gravity equation.

We show that changes in sentiment that aren’t related to fundamentals can drive persistent macroeconomic fluctuations even when all economic agents are rational. Changes in sentiment can also affect how fundamental shocks affect macroeconomic outcomes.

Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.

This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability.