1. Randomly draw cities (with replacement).
2. Keep all time series data for each drawn city.
3. Compute your estimate.
4. Repeat many times to get a valid confidence interval.

Use the Block Bootstrap:

Instead of resampling individual observations, resample entire cities to preserve time dependence.

1. Seattle appears multiple times in the data.
2. These data points aren’t independent. For example, if a GenAI-driven sales boom starts in Seattle, its impact persists over time, making observations correlated.

So how do we get valid confidence intervals while respecting these dependencies?

Let’s take a simple example:

We want to measure the impact of increasing ad spend in Seattle, but not in Portland.
We observe both cities before and after the marketing change.

But here’s the problem:

I was going to suggest East-West Germany comparison then remembered this great JEP piece on why caution is warranted www.aeaweb.org/articles?id=... @essobecker.bsky.social @lukasmergele.bsky.social

Soviets? Old Soviet Joke: We Pretend to Work,They Pretend to Pay Us.

Behavioral economics: people cannot add or subtract

Macroeconomics: *maybe* people cannot deal with the fact that the entire cross sectional distribution is a state variable

Historically, many people have believed that sources of power, like the vote, access to education, and the printing press, should be restricted to only the rich and powerful.

Today, some want you to believe that about data access.

Opening data access is less of a societal threat than closing it.

This is amazing! Would Claude outperform ChatGPT on this task, or is there another reason you chose it?