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Model risk is a constant danger for financial economists using interest-rate forecasts for the purposes of monetary policy analysis, portfolio allocations, or risk-management decisions. Use of multiple models does not necessarily solve the problem as it greatly increases the work required and still leaves the question "which model forecast should one use?"

There appears to be a disconnect between the importance of the zero bound on nominal interest rates in the real-world and predictions from quantitative DSGE models. Recent economic events have reinforced the relevance of the zero bound for monetary policy whereas quantitative models suggest that the zero bound does not constrain (optimal) monetary policy.

Standard monetary models adopt an infinite horizon with discounting. Testing these models in the lab requires implementing this horizon within a limited time frame. We compare three approaches to such an implementation and discuss their relative advantages.

This paper proposes a novel asymptotic least-squares estimator of multi-country Gaussian dynamic term structure models that is easy to compute and asymptotically efficient, even when the number of countries is relatively large—a situation in which other recently proposed approaches lose their tractability.

We revisit an old question: how do financial constraints affect the transmission of monetary policy to the real economy? To answer this question, we propose a simple empirical strategy that combines firm-level employment and balance sheet data, identified monetary policy shocks and survey data on financing activities.

I am honoured to address members of the Canadian Association for Business Economics. My remarks today will focus on critical issues that the Bank of Canada has studied over the past four years and how this research will inform our work as we move forward post crisis.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

Central banks play a pivotal role in well-functioning payments systems by providing liquidity via collateralized lending. This article discusses the role of collateral and haircut policy in central bank lending, as well as the distinguishing features of the central bank’s policy relative to private sector practices. It presents a model that explicitly incorporates the unique role of central banks in the payments system and argues that central banks must consider how their haircut policies affect the relative price and liquidity of assets, the market’s asset allocation, and the likelihood of participants to default. Furthermore, under extraordinary circumstances, there is a rationale for the central bank to temporarily reduce haircuts or broaden the list of eligible collateral to mitigate the shortage of liquidity in the market.

This paper continues the work started by Bolder and Stréliski (1999) and considers two alternative classes of models for extracting zero-coupon and forward rates from a set of observed Government of Canada bond and treasury-bill prices.

This paper proposes a theoretical framework to analyze the relationship between credit shocks, firm defaults and volatility, and to study the impact of credit shocks on business cycle dynamics.