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In this paper, we argue that differences in the cost structures across sectors play an important role in firms’ decisions to adjust their prices. We develop a menu-cost model of pricing in which retail firms intermediate trade between producers and consumers.

This article focuses on the role that loans backed by housing collateral play in amplifying housing booms and, more generally, procyclicality in the housing market. The author uses a model developed to include borrower and lender households, as well as a housing market, to examine the impact that altering the loan-to-value ratio (either permanently or countercyclically) might have on the volatility of house prices and mortgage debt.

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 model non-bank entry into fixed-income markets and state-dependent liquidity. Non-bank financial institutions improve liquidity more during normal times than in stress. Banks may become less reliable to marginal clients, exacerbating the difference in liquidity between normal and stressed times. Central bank lending during stress may limit this harmful division.

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.

Central banks face considerable uncertainty when conducting monetary policy. The COVID-19 pandemic brought this issue back to the forefront of policy discussions. We draw from academic literature to review key sources of uncertainty and how they affect the conduct of monetary policy.

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.

One of the most important factors that must be considered if countries are thinking about lowering the target level of inflation much below 2 per cent is the zero interest bound. Targeting inflation rates that are too low, the authors note, may restrict the ability of monetary policy to respond to economic shocks by limiting the amount by which interest rates can be eased.