Working Paper

FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

Abstract: 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.

Keywords: space-time autoregression; factor models; VAR; industrial diversity;

JEL Classification: C33; R11;

https://doi.org/10.20955/wp.2020.031

Status: Published in International Journal of Forecasting

Bibliographic Information

Provider: Federal Reserve Bank of St. Louis

Part of Series: Working Papers

Publication Date: 2020-08-14

Number: 2020-031

Note: Publisher DOI: https://doi.org/10.1016/j.ijforecast.2021.11.008

Related Works

• Working Paper Revision (2021-08-01) : FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications
• Working Paper Original (2020-08-14) : You are here.