Monetary Communication Rules
(with Amy Handlan)
Does the Federal Reserve follow a communication rule? We propose a simple framework to estimate communication rules, which we conceptualize as a systematic mapping between the Fed's expectations of macroeconomic variables and the words they use to talk about the economy. Using text analysis and regularized regressions, we find strong evidence for systematic communication rules that vary over time, with changes in the rule often being associated with changes in the economic environment. We also find that shifts in communication rules increase disagreement among professional forecasters and correlate with monetary policy surprise measures. Our method is general and can be applied to investigate systematic communication in a wide variety of settings.
NBER Monetary Economics Fall Meeting November 10, 2023 (see the livestream here)
Monetary Policy & Anchored Expectations - An Endogenous Gain Learning Model
Journal of Monetary Economics
This paper analyzes monetary policy in a model with a potential unanchoring of inflation expectations. The degree of unanchoring is given by how sensitively the public's long-run inflation expectations respond to inflation surprises. I find that optimal policy moves the interest rate aggressively when expectations unanchor, allowing the central bank to accommodate inflation fluctuations when expectations are well-anchored. Furthermore, I estimate the model-implied relationship that determines the extent of unanchoring. The data suggest that the expectations process is nonlinear and asymmetric: expectations respond more sensitively to large or downside surprises than to smaller or upside ones.
Paper (originally submitted, substantially longer version)
VMACS 2021 talk (10 minutes)
NBER Inflation Expectations 2022 talk (with discussion by Karthik Sastry, available until June 2022)
NBER Summer Institute 2022, Behavioral Macro Workshop talk (30 minutes, starting at 6:00:30, with discussion by Bruce Preston, available until August 2022)
Talking Over Time - Dynamic Central Bank Communication
Journal of Money, Credit & Banking
Investment and Communication Technologies and Medium-Run Fluctuations
(with Marco Brianti)
Journal of Economic Dynamics and Control
Work in Progress
Reputation for Competence
(with Amy Handlan)
The Feeling of the Age: A Quantitative Analysis of the Correlation between Novelistic and Economic Sentiment
(with literature scholar Daniella Gáti)
What predictive information do works of fiction contain for long-run economic fluctuations? We investigate this question in a two-step procedure. First, we conduct a sentiment analysis of prize-winning novels of the Pulitzer, National Book Award, PEN Faulkner, PEN Hemingway and Book Critics Circle awards from the years 1948-2018. We assign the novels quantitative sentiment scores. Second, we explore the correlation between the sentiment scores and macroeconomic aggregates such as GDP, total factor productivity, R&D expenditures, and so on. Once we have identified the frequencies where most of the comovement occurs via frequency domain methods, we analyze the cointegration relationships and the effects of innovations to literary sentiment using a structural Vector Error Correction model (VECM).
Do Long-Horizon Expectations Matter for New Keynesian Models?
In the literature on New Keynesian (NK) models with statistical learning, common practice is to write down the log-linearized first order conditions of the NK model and replace the objective with a subjective expectations operator ("Euler-equation approach"). An alternative approach following Preston (2005) involves obtaining model equations in which long-horizon expectations are explicitly spelled out ("long-horizon approach"). I investigate numerically what implications the two approaches have for model dynamics. While both lead to oscillatory impulse responses, the long-horizon approach results in much more volatility because the term structure of interest rate expectations, absent in Euler-equation learning, responds very sensitively to shocks.