As I’m teaching econometrics, I’m adding in handling-of-data issues. Examples from the last three years.
Don’t smear data sources without understanding the data
Don’t argue that the data haven’t exhibited something for a hundred years, when a hundred years of the data don’t exist.
The Availability of Quarterly GDP Data for the US: Memo to EJ Antoni
Don’t assert the data series don’t exist when they do indeed exist:
Understand what your deflators do — and do not — include before making inferences (tariff pass through edition).
Don’t assert the existence of a vast conspiracy, when simple statistical sampling error could explain results (not that conspiracies don’t exist: just think “Epstein”).
Don’t make unsubstantiated assertions that are countered by the bulk of peer reviewed studies.
Don’t use month/month growth comparisons when the data are noisy:
Heritage Chief Economist Interprets Biden vs. Trump Employment Trends
When trying to characterize a phenomenon (e.g., inflation), do not rely on one, largely undocumented, data series:
Truflation Chief Economist: “Less Than 1 Percent Inflation? Yes.”
Don’t assert the only definition of a recession is two consecutive quarters of negative GDP growth:
Why Friends Don’t Let Friends Define Recessions as Two Consecutive Quarters of Negative GDP Growth
Report regression statistics (e.g., R-squareds) if you’re going to cite regression results.
Keep track of what units your variables are measured in, including when running regressions.
Is the Sensitivity of Muni Bond Rates to 10 Year Treasury 0.04?
If you are reporting provocative empirical results, document your data and data series construction.
The second edition of rookie economist errors.
Yet more errors:
Don’t be casual about estimated trends.
Don’t make policy analysis based on not-statistically-significant parameters.