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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.

The authors use simple new finite-sample methods to test the empirical relevance of the New Keynesian Phillips curve (NKPC) equation.

The paper reviews evidence from the economic literature on the nature of the relationship between excess capacity and inflation, better known as the Phillips curve. In particular, we examine the linearity of this relationship. This is an important issue in the current economic context in which advanced economies are approaching or exceed their potential output.

This note discusses insights from historical launches of new payment methods and related laboratory experiments on the potential adoption and use of a central bank digital currency in the Canadian context.

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).

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.

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.

Latency delays—known as “speed bumps”—are an intentional slowing of order flow by exchanges. Supporters contend that delays protect market makers from high-frequency arbitrage, while opponents warn that delays promote “quote fading” by market makers. We construct a model of informed trading in a fragmented market, where one market operates a conventional order book and the other imposes a latency delay on market orders.