The monthly policy news shock series from Nakamura and Steinsson (2018). Each monthly observation is the sum of high-frequency FOMC-event surprises occurring within the month. The surprise at each FOMC announcement is the first principal component of changes in five interest-rate futures (Fed Funds and Eurodollar) in a 30-minute window bracketing the announcement. Months with no scheduled FOMC meeting are coded as zero.
Format
A data frame with columns:
- date
Date. First day of the observation month.- shock
numeric. Policy news shock, scaled to one-year Treasury-yield equivalents (percentage points).- series
character. Series identifier"nakamura_steinsson".
Source
Nakamura, E., & Steinsson, J. (2018). "High-Frequency Identification of Monetary Non-Neutrality: The Information Effect." Quarterly Journal of Economics 133(3): 1283-1330. doi:10.1093/qje/qjy004 . Replication archive on Harvard Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HZOXKN (CC0 1.0 Universal public domain dedication).
Details
Scaling. The raw first principal component is rescaled so that a unit change equals the contemporaneous change in the one-year nominal Treasury yield (NS Section II.B). Magnitudes are therefore not directly comparable to Kuttner (2001) basis-point fed-funds surprises or to raw FF1 / FF4 surprises without rescaling.
Interpretation caveat. NS frame their policy-news shock as evidence of a "Fed information effect": hawkish surprises raise private-sector growth forecasts. Bauer and Swanson (2023, AER 113(3)) argue the pattern is better explained by the Fed and professional forecasters reacting to the same pre-meeting public data ("Fed response to news"). Users estimating causal macro effects of policy should consider bauer_swanson (MPS_ORTH) or miranda_agrippino_ricco as alternatives that address this bias.
Unscheduled meetings. Inter-meeting cuts (notably 22 January 2008 and 8 October 2008) are included in the series and drive a large share of sample variance.