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Real rigidities that limit the responsiveness of real marginal cost to output are a key ingredient of sticky price models necessary to account for the dynamics of output and inflation. We argue here, in the spirit of Bils and Kahn (2000), that the behavior of marginal cost over the cycle is directly related to that of inventories, data on which is readily available.

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).

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.

We present a two-country model featuring risky lending and cross-border interbank market frictions.

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.

In recent years, there has been a lot of interest in Taylor-type rules. Evidence in the literature suggests that Taylor-type rules are optimal in a number of models and are fairly robust across different models.

Digital currencies have attracted strong interest in recent years and have the potential to become widely adopted for use in making payments. Public authorities and central banks around the world are closely monitoring developments in digital currencies and studying their implications for the economy, the financial system and central banks.

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.

The household debt-to-disposable income ratio in Canada increased from 110 per cent in 1999 to 127 per cent in 2007. This increase has raised questions about the ability of households to service their increased debt if faced with a negative economic or socio-economic shock.