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We present a model in which banks and other financial intermediaries face both occasionally binding borrowing constraints and costs of equity issuance. Near the steady state, these intermediaries can raise equity finance at no cost through retained earnings.

We estimate the effects of economic uncertainty on time use and discuss its macroeconomic implications. We develop a model to demonstrate that substitution between market and non-market work provides an additional insurance margin to households, weakening precautionary savings and labour supply and lowering aggregate demand, ultimately amplifying the contractionary effects of uncertainty.

We analyze the transmission of monetary policy during different phases of a sectoral demand reallocation episode when there are frictions to increasing production in a sector. Monetary policy is more effective in reducing inflation when a larger proportion of sectors are expanding or expect to expand in the near future.

The uncertainty around future changes to the Federal Reserve target rate varies over time. In our results, the main driver of uncertainty is a “path” factor signaling information about future policy actions, which is filtered from federal funds futures data.

Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash.

We model the asset-opacity choice of an intermediary subject to rollover risk in wholesale funding markets. Greater opacity means investors form more dispersed beliefs about an intermediary’s profitability.

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes.

Investors who trade based on good research are said to be the backbone of stock markets: They conduct research to discover the value of stocks and, through their trading, guide financial prices to reflect true value. What can make their job difficult is that high-speed, short-term traders could use machine learning and other technologies to infer when informed investors are trading.

We model the introduction of a new payment method, e.g., e-money, that competes with an existing payment method, e.g., cash. The new payment method involves relatively lower per-transaction costs for both buyers and sellers, but sellers must pay a fixed fee to accept the new payment method.

We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.