A new working paper for holiday reading! @peder_isager and I provide an introduction to three-sided testing, a framework for testing an estimate's practical significance. We offer a tutorial, Shiny app, + commands/code in #Rstats, Jamovi, and #Stata (🔗 below!) 1/9
#EconTwitter
Equivalence testing lets us test whether estimates are stat. sig. bounded beneath practically negligible effect size Δ (e.g., pink estimate). But estimates can be both stat. sig. diff. from zero and stat. sig. bounded beneath Δ. 2/9
Estimates can also be stat. sig. bounded outside of Δ (e.g., blue estimate). What should we conclude about estimates like these blue/orange estimates? Standard equivalence testing frameworks don't give us clear answers. We introduce researchers to a framework that does. 3/9
The three-sided testing (TST) framework combines two-sided minimum effects tests for inferiority/superiority with the two one-sided tests (TOST) equivalence testing procedure. TST can provide stat. sig. evidence that estimates are practically significant, or practically = 0. 4/9
This procedure was developed by Goeman, @aldosolari, & Stijnen (2010), who show that by partitioning estimates' parameter spaces into disjoint regions, TST controls error rates over all three of its tests w/ no penalty to power. 5/9
@aldosolari Practical significance conclusions about an estimate can be easily inferred from double-banded confidence intervals that combine the estimate’s (1 - α) CI (e.g., its 95% CI) with its (1 - 2α) CI (e.g., its 90% CI). 6/9
@aldosolari To make things easy, we offer the ShinyTST app, a point-and-click Shiny app that tells you which test/confidence interval is relevant, provides p-values, and visualizes test results given an estimate, standard error, and SESOI. 7/9
@aldosolari We also offer the tst() command in the eqtesting R package, the tsti command in Stata, and Jamovi code. You can visit the paper to find download instructions for all, + guidelines for implementation. We hope you find it useful! (8/9)
@aldosolari For those on #EconTwitter, in addition to our PsyArXiv paper, we’ve also deposited a version into the Tinbergen Institute Discussion Paper Series: (9/9)
Do real stakes/incentives matter in experiments? Recent studies say they don’t. My new paper shows that these studies’ results — and those of most hypothetical bias experiments — are uninformative when we care about experimental treatment effects. 1/x
🔗: papers.tinbergen.nl/24070.pdf
Historically, experimental economists virtually always tied experimental choices to real stakes/payoffs to improve generalizability. That’s changing: many economists now use hypothetical stakes in online experiments + large-scale survey experiments. 2/x
There’s also recently been a wave of new studies showing that certain outcomes don’t stat. sig. differ between real-stakes and hypothetical-stakes experiments. These results are affecting thinking at the highest levels of experimental economics. 3/x
🧵 on my replication of Moscona & Sastry (2023, QJE).
TL;DR: MS23 proxy 'innovation exposure' with a measure of heat. Using direct innovation measures from the paper’s own data decreases headline estimates of innovation’s mitigatory impact on climate change damage by >99.8%. 1/x
Moscona & Sastry (2023) reach two findings. First, climate change spurs agricultural innovation. Crops with croplands more exposed to extreme heat see increases in variety development and climate change-related patenting. 2/x academic.oup.com/qje/article/13…
Second, MS23 find that innovation mitigates damage from climate change. They develop a county-level measure of 'innovation exposure' and find that agricultural land in counties with higher levels of 'innovation exposure' is significantly less devalued by extreme heat. 3/x
My paper is out in @PNASNews! I replicate a paper on the impact of COVID vaccine mandates on vaccine uptake. Removing a single bad control variable sign-flips several of the paper’s headline results. The reply’s findings are also not robust. 1/x pnas.org/doi/10.1073/pn…
@PNASNews Rains & Richards (2024) — henceforth RR24 — reach two findings. First, RR24 claim that difference-in-differences estimates show that US state COVID vaccine mandates had imprecise impacts on COVID vaccine uptake. 2/x pnas.org/doi/10.1073/pn…
@PNASNews Second, RR24 find that states that mandated COVID vaccination statewide now see lower uptake of COVID boosters and both adult + child flu vaccines than states that banned local COVID vaccine mandates. 3/x
🚨 WP alert! 🚨 I design equivalence tests for running variable (RV) manipulation in regression discontinuity (RDD), show that serious RV manipulation can't be ruled out in lots of published RDD research, and offer the lddtest command in Stata/R. 1/x
Credible RDD estimation relies on the assumption that agents can’t endogenously sort their RVs to opt themselves into or out of treatment. If they can, then RDD estimates are confounded: agents who manipulate RVs are likely different in important ways from agents who don't. 2/x
Such manipulation often causes jumps in RV density at the cutoff, which can either come from genuine distributional distortions or from strategic reporting. E.g., consider the French examples below. 3/x