Search Results
Newsletter
Data Revisions with FRED®
This Page One Economics Data Primer describes the reasons data are revised and updated. More accurate data facilitate better decisionmaking.Learn how FRED aggregates the latest data and ALFRED captures previous versions of the data.
Discussion Paper
First Impressions Can Be Misleading: Revisions to House Price Changes
An assiduous follower of the national house price charts that the New York Fed maintains on its web page may have noticed that we appear to be rewriting history as we update the charts every month. For example, last month we reported that the median twelve-month house price change across all counties for December 2012 was 3.68 percent. However, this month, we indicate that this same median change for December 2012 was instead 3.45 percent. Why the change? Was the earlier reported number a mistake that we simply corrected this month? If not, what explains the revision to the initial report?
Working Paper
Predicting Benchmarked US State Employment Data in Real Time
US payroll employment data come from a survey of nonfarm business establishments and are therefore subject to revisions. While the revisions are generally small at the national level, they can be large enough at the state level to substantially alter assessments of current economic conditions. Researchers and policymakers must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data on the universe of workers some 5 to 16 months after the reference period. This paper develops and tests a state space model that predicts ...
Discussion Paper
Inflation Persistence: Dissecting the News in January PCE Data
This post presents updated estimates of inflation persistence, following the release of personal consumption expenditure (PCE) price data for January 2023. The estimates are obtained by the Multivariate Core Trend (MCT), a model we introduced on Liberty Street Economics last year and covered most recently here and here. The MCT is a dynamic factor model estimated on monthly data for the seventeen major sectors of the PCE price index. It decomposes each sector’s inflation as the sum of a common trend, a sector-specific trend, a common transitory shock, and a sector-specific transitory shock. ...