Author’s note: charts have been updated after the first posting to reflect recent data.

Raw numbers are horrific. Per capita statistics show a more complete picture of the distribution of COVID cases, but they may not be the best measure either. For non-communicable conditions — cancer, cataracts, constipation — “X cases per Y people” does provide a useful metric for geographic comparison. But COVID is highly transmittable. New York City broke out faster and to a greater extent than [fill-in-the-blank rural city in rural state]. …


We all want to know if COVID lockdowns actually work. They’re a huge price to pay even if they do and all the more painful if they don’t.

To understand a relationship between any cause and effect, both must be measured. We need to understand the severity of lockdowns — the cause — before we can do any regression or correlation analysis with death toll or economic impact — the effects. Metrics for these effects exist, but understanding the cause poses a challenge. Until now.

The index I present here — created from raw data with thousands of lines of…


Here’s a simple chart. I had fun making it, and am proud of my shrewd code and above-average-but-not-stellar visual design. There’s even a quick message: annual bond returns fair slightly better under Republican presidents than Democratic presidents, but vice-versa for S&P 500 returns.

Done deal, right? Not so fast.

It’s foolish to think markets are exclusively driven by real-time political policy. I don’t know the exact decomposition, but it’s safe to say that many dynamics beyond the political party of the president affect market returns: the political party of the previous administration, the administration before that, the one before that……


Carelessly claiming causation causes confusion

It’s not because of an off-color tweet by Sir Clive W. J. Granger before his rise to fame, unlike many modern-day cancellations.

It’s because Granger himself suggested that the term “Granger Causal” is inappropriate to describe the effect observed. He wrote this a decade after his flagship paper (which, matching his humble personality, did not suggest an eponymous name) and four and a half decades later in his Nobel Prize lecture. He, along with the broader statistics community, knew that correlation is not synonymous with causation. Granger-causality should be “Granger-correlation-which-sometimes-exists-because-of-causation”, or alternatively, “temporal correlation.”

Granger’s words in the 1977 text “Forecasting Economic Time Series” show his hesitancy.

As defined, Granger-causality is a very…


Charting the route of the economists whose job is to think like Google Maps

“If we could first know where we are and whither we are tending, we could better judge what to do and how to do it.” — Abraham Lincoln

When you hear “They say the economy is on the way back up,” who is “they” and why do they say that? There are so many components from so many industries that produce so much data that it can be hard to know the actual state of the economy. One measure is the gross domestic product (GDP), which is the total value of all goods and services traded in the country.¹ Economists who try to make sense of uncertainty and make informed analyses about the state of the economy are called nowcasters: formally known as macro-econometricians, casually known as magicians.

Michael Boerman

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