Model: Federal Reserve Bank of San Francisco/Wilson - FRBSF_Wilson-Econometric

    Name: Econometric
    Abbreviation: FRBSF_Wilson-Econometric
    Owner: covid19hub
    Team name: Federal Reserve Bank of San Francisco/Wilson
    Description: Econometric county panel data model, derived from SIR model, with transmission rate assumed to be function of weather and mobility.
    Contributors: Daniel J. Wilson (Federal Reserve Bank of San Francisco, Economic Research) <daniel.wilson@sf.frb.org>, Samuel R. Tarasewicz (Federal Reserve Bank of San Francisco, Economic Research) <Sam.Tarasewicz@sf.frb.org>
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: https://www.frbsf.org/economic-research/publications/working-papers/2020/23/
    Methods: We estimate an econometric county panel data model based on the standard SIR model. The econometric model relates the change in log active (new) infections between now and some future horizon to the current transmission rate and the susceptible share of the population. The current transmission rate is assumed to be a function of individuals’ mobility behavior and weather (temperature and precipitation). County level mobility is measured using the Mobility and Engagement Index (MEI) constructed by the Federal Reserve Bank of Dallas from mobile device geolocation data provided by Safegraph. County level weather data comes from weather station readings on daily-high temperatures and precipitation from the National Oceanic and Atmospheric Administration (NOAA), following the methodology in Wilson (2019). For diseases that convey long-lasting immunity to those who have recovered from infection, the susceptible share is simply one minus the share of the local population that have previously been infected. We assume this share in a given county is proportional to cumulative confirmed cases to date as a share of the county’s population. This proportionality also implies that the change in log active infections can be proxied by the change in log active cases. Aside from mobility, weather, and accumulated natural immunity, there are two other important elements of the econometric model. The first is lagged infection growth, allowing for the possibility that lagged infection growth is predictive of subsequent infection growth. The second additional element is county-specific intercepts (fixed effects), which allow each county to have a different mean level of infection growth, independent of the other elements of the model.
    Home: https://www.frbsf.org/economic-research/indicators-data/covid-19-forecasts-by-county/
    Auxiliary data: Missing

    Forecasts (5)

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    2021-03-22 2021-03-22-FRBSF_Wilson-Econometric.csv 2021-03-22 18:49:07 UTC 2021-03-22
    2021-03-18 2021-03-18-FRBSF_Wilson-Econometric.csv 2021-03-22 06:51:20 UTC 2021-03-22
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