Ekaterina Peneva, Jeremy Rudd, and Daniel VillarThis paper examines the Board staff’s inflation forecast misses over the years following the COVID-19 outbreak, focusing on a timeline of what staff members knew when and lessons learned along the way.
Brent Bundick, Isabel Cairó, and Nicolas Petrosky-NadeauThis paper reviews recent academic studies to assess the implications of adopting a shortfalls, rather than a deviations, approach to pursuing maximum employment. Model-based simulations from these studies suggest three main findings. First, shortfalls rules generate inflationary pressure relative to deviations rules, which offsets downward pressure on inflation stemming from the presence of the effective lower bound.
Grey Gordon, Julio Ortiz, and Benjamin SilkWe examine the experience of conducting reviews of monetary policy frameworks in the major advanced foreign economies since the Federal Open Market Committee's 2019–20 review. We find that periodic reviews are becoming the norm and have often been motivated by similar developments and challenges as those facing the Federal Reserve.
François Gourio, Benjamin K. Johannsen, and David López-SalidoIn this paper, we describe the Federal Reserve’s 2019–20 review of its monetary policy framework. First, we discuss the historical background of and motivation for the review.
Hitesh Doshi, Hyung Joo Kim, and Sang Byung SeoThe identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We show that the interbank market can help characterize the time variation in disaster risk. We propose a risk-based model in which macroeconomic disasters are likely to coincide with interbank market failure. Using interbank rates and their options, we estimate our model via MLE and filter the short-run and long-run components of disaster risk.
Mark CarlsonThere was a banking panic in New Mexico in early 1924 when about one-fourth of the banks in the state closed temporarily or permanently amid widespread runs. The Federal Reserve used both high profile and behind the scenes operations to calm the panic. This paper provides a history of this episode and explores how conspicuous and inconspicuous aspects of the Federal Reserve's response interacted to bolster confidence in the banking system.
Samin Abdullah and Manjola TaseWe find that an increase in policy rate uncertainty is associated with an increase in MMF portfolio allocations towards assets with shorter-dated maturities. We also find that the direction of uncertainty matters: MMF portfolio maturity is more sensitive to uncertainty when it relates to changes in expectations for a larger increase or a smaller decrease in the policy rate than when it relates to changes in expectations for a smaller increase or a larger decrease in the policy rate.
Hie Joo Ahn and Yunjong EoThis paper empirically investigates sources of hysteresis, focusing on downward nominal wage rigidity and the gender gap in the labor market, using U.S. state-level payroll employment data. Employing a Bayesian Markov-switching model of business cycles, we identify U-shaped and L-shaped recessions, which correspond to quick recoveries and hysteresis, respectively.
Ece Fisgin, Joaquin Garcia-Cabo, Alex Haag, and Mitch LottWe present a principal component analysis of euro area labor market conditions by combining information from 22 labor market indicators into two comprehensive series. These two novel indicators provide a systematic view of the current state and forward-looking direction of the euro-area labor market, respectively, and demonstrate superior forecasting performance compared to existing indicators.
Rehim KilicThis paper fills an important gap in the volatility forecasting literature by comparing a broad suite of machine learning (ML) methods with both linear and nonlinear econometric models using high-frequency realized volatility (RV) data for the S&P 500. We evaluate ARFIMA, HAR, regime-switching HAR models (THAR, STHAR, MSHAR), and ML methods including Extreme Gradient Boosting, deep feed-forward neural networks, and recurrent networks (BRNN, LSTM, LSTM-A, GRU).
