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Macroeconomic projections and risk analyses play an important role in guiding monetary policy decisions. Models are integral to this process. This paper discusses how the Bank of Canada brings research models and lessons learned from those models into the central bank projection environment.

We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms.

A low level of the neutral rate of interest increases the likelihood that a central bank’s policy rate will reach its effective lower bound (ELB) in future economic downturns. In a low neutral rate environment, using an extended monetary policy toolkit including forward guidance helps address the ELB challenge. Using the Bank’s Terms-of-Trade Economic Model, we assess the benefits and limitations of a state-contingent forward guidance implemented within a flexible inflation targeting framework.

Using Canadian matched employer-employee data, we show that working hours of different workers are gross complements in production rather than perfect substitutes, as is typically assumed by macroeconomic models of production.

This article summarizes the proceedings of an international research conference hosted by the Bank of Canada in November 2000. The conference marked the fiftieth anniversary of Canada's adoption of a flexible exchange rate, and its title recognizes the seminal contribution of Professor Milton Friedman's article "The Case for Flexible Exchange Rates." His keynote address to the conference is also summarized in the article. The conference papers re-examine many of the arguments raised by Friedman using recent developments in economic theory and econometric techniques. They investigate the experience of a wide range of industrialized and emerging-market economies. The main findings are that a strong case can be made for flexible exchange rates in economies that are large commodity exporters and that have credible low-inflation monetary policies and relatively well-developed financial systems.

We show that US banks price deposits almost uniformly across their branches and that this pricing practice is more important than increases in local market concentration in explaining the deposit rate dynamics following bank mergers.

We propose an open-economy New Keynesian model with financial integration that allows financial intermediaries to hold foreign long-term bonds. We study the implications of financial integration on monetary policy transmission. Among various aspects of financial integration, the bond duration plays a major role. These results hold for conventional and unconventional monetary policies.

Many emerging-market economies (EMEs) have significantly improved their macroeconomic fundamentals and undergone structural reforms since the Asian crisis. These developments have enhanced the composition of capital flows to EMEs through an improved debt structure, a larger share of capital flows as foreign direct investment, and greater access to international debt markets for corporations in EMEs. Structural changes in the global financial landscape have also increased capital flows, bringing economic and financial benefits to EMEs. During the recent financial crisis, however, the opening up of capital accounts and increased financial and trade linkages left many countries vulnerable to external disruptions. Countries with sound fundamentals have weathered the crisis relatively well. Policy-makers in EMEs need to implement policies that support capital flows and ensure that controls imposed to deal with detrimental outflows during periods of stress or rapid inflows are only temporary.

Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.

Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems.