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After the financial crisis of 2007–09, many jurisdictions introduced new banking regulations to make banks more resilient and less likely to fail. These regulations included tighter limits for the quality and quantity of bank capital and introduced minimum standards for liquidity. But what was the impact of these changes?

Can monetary policy influence the neutral real interest rate (r-star)? Using a new statistical model, we show that interest rate hikes tend to lower r-star and long-run growth, but that monetary policy explains only a small share of the long-run decline in r-star.

We study the cross-section of equilibrium returns on safe assets using a tractable asset pricing model with a micro-founded demand for liquidity and multiple safe assets with heterogeneous transaction costs. We test the main predictions of our theory using a novel measure of relative (in)convenience yields in the US Treasury market.

We investigate the unintended consequences of the Home Affordable Refinance Program (HARP). Originally designed to help borrowers refinance after the 2008–09 global financial crisis, HARP inadvertently strengthened the market power of incumbent lenders by creating a cost advantage for them. Despite a 2013 policy rectifying this cost advantage, we still find significant welfare losses for borrowers.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

This paper studies optimal discretionary monetary and fiscal policy when the lower bound on nominal interest rates is occasionally binding in a model with nominal rigidities and long-term government debt. At the lower bound it is optimal for the government to temporarily reduce debt.

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.

The consumption boom-bust cycle in the 2000s coincided with large fluctuations in the volume of home equity borrowing. Contrary to conventional wisdom, I show that homeowners largely borrowed for residential investment and not consumption.

Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.

We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.