Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision

A-Tier
Journal: Journal of Business & Economic Statistics
Year: 2017
Volume: 35
Issue: 3
Pages: 420-433

Score contribution per author:

4.036 = (α=2.02 / 1 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box–Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically estimated model of data revisions for U.S. output growth is used to investigate small-sample properties.

Technical Details

RePEc Handle
repec:taf:jnlbes:v:35:y:2017:i:3:p:420-433
Journal Field
Econometrics
Author Count
1
Added to Database
2026-01-25