Hello, first time poster here, interested in forecasting.
I am working with a city-sized population (~150k residents, ~30 tracts) in the San Francisco Bay Area, for which I can retrieve ACS population estimates by race/ethnicity (from B01003) for a couple of overlapping intervals (2011-2015, …, 2014-2018).
The proportions are fairly stable, but I’m more interested in the future — specifically, in 2021-2025 — than the past.
As an R user, I could try to adapt some generic forecasting tools, like those in Rob Hyndman's {fable} package, to generate estimates for the near future. But should I be guessing at which tools and approaches to use, or has someone already done a pretty robust job somewhere else that could be adapted or applied? (Note: not asking for R code, just recommendations for an approach.)
Methods that would generate predictive intervals, or at least some metric of uncertainty/confidence, would be ideal. All suggestions would be welcome! I'm hoping to be able to apply the same technique(s) to other city-sized regions in the future, even after Decennial counts become available.
Thank you in advance for your time and attention.
Very best, David
Hey David:
Interesting question! There are a few different techniques you could try -- one would be some sort of linear-based forecast model to predict total population (from pop estimates) and ACS-derived…
Hi David -
I second all of the recommendations above, but also wonder: Have you talked with ABAG about their projections? Do they not have what you need?
I do some California-specific forecasting work…
Hi Beth Jarosz, that is great advice. After you mentioned ABAG, I dug up some copies of tract-level ABAG forecasts. They’re vintage 2013 and aren't split by race/ethnicity. But I will definitely check totals…
Welcome to the group David!
I would start by developing a conceptual model of what is driving demographic changes in this community. Is it the birth rate of different race/ethnic groups? Death rate? Immigration and emigration rates? General economic growth? Housing availability? Transportation? You could use historic data to calculate trends for the important components. Then model various scenarios for how policy changes might impact the historic trends.