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In this paper, the authors survey some of the recent techniques proposed in the literature to measure the trend component of output or potential output. Given the reported shortcomings of mechanical filters and univariate approaches to estimate potential output, the paper focusses on three simple multivariate methodologies: the multivariate Beveridge-Nelson methodology (MBN), Cochrane's methodology (CO), and the structural VAR methodology with long-run restrictions applied to output (LRRO).

The author reviews the theoretical and empirical literature to examine the traditional perception that the following trade-off exists between economic efficiency and stability in the banking system: a competitive banking system is more efficient and therefore important to growth, but market power is necessary for stability in the banking system.

One way of internalizing the externalities that each individual bank imposes on the rest of the financial system is to impose capital surcharges on them in line with their systemic importance.

The COVID-19 pandemic has caused an atypical recession in which some sectors of the economy boomed and others collapsed. This required a unique fiscal policy reaction to both support firms and stimulate activity in sectors with slack. Was fiscal policy able to get where it was needed? Mostly, yes.

This paper reviews the existing theoretical and empirical literature addressing the benefits of low inflation. The ultimate goal is to arrive at a set of benefits in which a monetary authority can have genuine confidence. I argue that the current state of economic research—both empirical and theoretical—provides little basis for believing in significant observable benefits […]

The good forecasting performance of factor models has been well documented in the literature. While many studies focus on a very limited set of variables (typically GDP and inflation), this study evaluates forecasting performance at disaggregated levels to examine the source of the improved forecasting accuracy, relative to a simple autoregressive model. We use the latest revision of over 100 U.S. time series over the period 1974-2009 (monthly and quarterly data).

This article focuses on a quantitative method to identify financial system vulnerabilities, specifically, an imbalance indicator model (IIM) and its application to Canada. An IIM identifies potential vulnerabilities in a financial system by comparing current economic and financial data with data from periods leading up to past episodes of financial stress. It complements other sources of information - including market intelligence and regular monitoring of the economy - that policy-makers use to assess vulnerabilities.

This paper attempts to reduce the uncertainty about the dynamics of the monetary transmission mechanism. Central to this attempt is the identification of monetary policy shocks. Recently, VAR approaches that use over-identifying restrictions have shown success in isolating such shocks.

We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.