Search

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.

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.

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.

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.

This paper theoretically and empirically examines the price impacts of liquidations in DeFi and how different liquidation mechanisms affect the price impacts.

This paper studies the music streaming industry and argues that having exclusive rights granted by copyright law drives firms to offer exclusive content to lock in customers. I employ theoretical and descriptive empirical analysis, along with a dynamic structural model, to support the argument and explore policies for improving competition.

Many explanations for the decline in real interest rates over the last 30 years point to the role that population aging or rising income inequality plays in increasing the long-run aggregate demand for assets. Notwithstanding the importance of such factors, the starting point of this paper is to show that the major change driving household asset demand over this period is instead an increased desire—for a given age and income level—to hold assets.

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 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.

When lenders cannot directly identify behavioural and rational borrowers, they use type scoring to track the likelihood of a borrower’s type. This leads to the partial pooling of borrowers, which results in rational borrowers subsidizing borrowing costs for behavioural borrowers. This, in turn, reduces the effectiveness of regulatory policies that target mistakes by behavioural borrowers.