FRED-SD: A real-time database for state-level data with forecasting applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor-market, production, and housing variables. We conduct two sets of re...

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Bibliographic Details
Published in:	International journal of forecasting Vol. 39; no. 1; pp. 279 - 297
Main Authors:	Bokun, Kathryn O., Jackson, Laura E., Kliesen, Kevin L., Owyang, Michael T.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-01-2023
Subjects:	Bayesian VAR Factor model Forecasting Real-time vintages Space–time autoregression Bayesian VAR Space–time autoregression Real-time vintages Factor model Forecasting
Online Access:	Get full text
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Summary:	We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor-market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments suggest that large models with industrially disaggregated data tend to have higher predictive ability for industrially diversified states. For national-level data, we find that forecasting and aggregating state-level data can outperform a random walk but not an autoregression. We compare these real-time data experiments with forecasting experiments using final-vintage data and find very different results. Because these final-vintage results are obtained with revised data that would not have been available at the time the forecasts would have been made, we conclude that the use of real-time data is essential for drawing proper conclusions about state-level forecasting models.
ISSN:	0169-2070 1872-8200
DOI:	10.1016/j.ijforecast.2021.11.008