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3128 Results

Seasonal Adjustment of Weekly Data

Staff discussion paper 2024-17 Jeffrey Mollins, Rachit Lumb
The industry standard for seasonally adjusting data, X-13ARIMA-SEATS, is not suitable for high-frequency data. We summarize and assess several of the most popular seasonal adjustment methods for weekly data given the increased availability and promise of non-traditional data at higher frequencies.

Noisy Monetary Policy

Staff working paper 2018-23 Tatjana Dahlhaus, Luca Gambetti
We introduce limited information in monetary policy. Agents receive signals from the central bank revealing new information (“news") about the future evolution of the policy rate before changes in the rate actually take place. However, the signal is disturbed by noise.

High-Frequency Cross-Sectional Identification of Military News Shocks

Staff working paper 2025-27 Francesco Amodeo, Edoardo Briganti
We identify and quantify fiscal news shocks, compiling events (2001–2023) that altered the expected path of U.S. defense expenditure. For each event, we estimate market-implied shifts in expected spending. A shift-share analysis yields a two-year, metropolitan statistical area–level GDP multiplier of approximately 1 for U.S. military build-ups.

Information, Prices and Buyer Entry

Staff working paper 2026-4 Mei Dong, Janet Hua Jiang, Ling Sun
In markets with costly buyer entry, information transparency about prices draws in buyers, increasing demand-side competition and putting upward pressure on prices. We show that this buyer entry effect may dominate seller competition as emphasized by conventional wisdom and prices and markups may rise with information transparency.
Content Type(s): Staff research, Staff working papers JEL Code(s): D, D4, D40, D8, D83, L, L1, L11 Research Theme(s): Models and tools, Economic models

Consumer Search, Productivity Heterogeneity, Prices, Markups, and Pass-through: Theory and Estimation

Staff working paper 2024-50 Alex Chernoff, Allen Head, Beverly Lapham
We develop and estimate a search model in which identical consumers trade with price-setting firms that differ in productivity. We use the estimated model to characterize the qualitative and quantitative differences in prices and markups across firms. We explore how individual firms respond to changes in cost and demand and how they pass these through to their prices and markup.

Measuring the AI Economy

Staff working paper 2026-20 Anton Korinek, Patrick McKelvey
We construct a macroeconomic estimate of total AI production in the United States, combining inference and R&D/training activities with quality adjustments to account for algorithmic progress. We then develop a nascent framework for "AI GDP" that tracks the AI economy as a coherent whole, complementing traditional national accounts.
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