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

Author

Listed:
  • Kathryn Bokun
  • Laura E. Jackson
  • Kevin L. Kliesen
  • Michael T. Owyang

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.

Suggested Citation

  • Kathryn Bokun & Laura E. Jackson & Kevin L. Kliesen & Michael T. Owyang, 2020. "FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications," Working Papers 2020-031, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:88720
    DOI: 10.20955/wp.2020.031
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    References listed on IDEAS

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    More about this item

    Keywords

    space-time autogregression; factor model; VAR; industrial diversity;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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