Jack Fitzgerald

@jackfitzgerald.bsky.social

1508 Followers 265 Following

Economics PhD candidate at Vrije Universiteit Amsterdam and the Tinbergen Institute. Working on applied econometrics, replication, and economics of science. https://jack-fitzgerald.github.io. Likes/reposts aren’t endorsements, views are my own.

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Subject line: Your Article Is Needed For Page Layout Hi Doctor, Hey. We are writing to you right away about the invitation we sent you in March 2026. We haven't received your manuscript yet, even though we've talked about it before. We're very close to finishing the next issue of [redacted]. This is your last chance to say for sure that you will be there. We don't want to close this issue without giving you one last chance to send in your most recent work because your academic work has always been very useful to the scientific community. We really appreciate your participation, and sending in your work quickly will make our next publication much better. The last day to send in your work is May 29, 2026. If you confirm right away, you can get more time. We respectfully ask that you confirm by responding with one of the following choices so that we can effectively resolve the matter: Yes - I'm interested. Kindly move on to the following steps. No - I won't be taking part right now. We will accept articles like Original Research, Reviews, Case Reports/Case Studies, Opinions and Perspectives, Images/Short Communications, and other things like these. A short cover letter should be included with your submission. To finish the editorial schedule and avoid more follow-ups, we need your confirmation within the next 24 hours. We'll think you're not interested in this issue if you don't respond right away. Your work will not only make the journal better, but it will also show how important you are to the scientific community. We really hope to include your important work. Thank you for paying attention right away. We are waiting for you to confirm. Best of luck, [redacted]
Figure 4 in the paper. Table version: Appendix Table A2.
Figure 1 in the paper.
Figure 4 in the paper. Table version: Appendix Table A2.
Table 4 in the paper.
Table 1 in the paper.
Source: https://firmbackbone.nl/
Source: https://lisa.bij12.nl/
Source: sketchplanations.com/sampling-bias
Paper title and abstract.
Line graphs displaying time series of the raw number of terrorist attacks over time in each of 28 EU member states. A red box highlights the time series for the United Kingdom. Based on WEA's data.
Image of panel (a) in Table 1 from the Matters Arising.
4x2 array of scatterplots with OLS lines of best fit. In the first row of graphs, the y-axis represents values of NewArrest; the x-axis of the left (right) graph represents values of (IHS) arrest rates. In the second row of graphs, the y-axis represents values of CharSin; the x-axis of the left (right) graph represents values of (IHS) charge rates. In the third row of graphs, the y-axis represents values of ConvictionSin; the x-axis of the left (right) graph represents values of (IHS) convictionrate. In the fourth row of graphs, the y-axis represents values of SentenceSin; the x-axis of the left (right) graph represents values of (IHS) sentence. In the top two graphs, the OLS line of best fit is downward-sloping, whereas it is upward-sloping in the bottom six graphs. No line of best fit in the second column of graphs perfectly intersects all points.
Image of the first few observations from WEA's replication dataset. Red boxes highlight variables ConvictionSin, SentenceSin, CharSin, and NewArrest. Source: https://zenodo.org/records/8196717.
Displays two scatterplots with OLS lines of best fit. In each graph, the y-axis shows values of DVsin. In the left scatterplot, the x-axis is the raw attack rate (attacks/population in hundreds of thousands), whereas in the right scatterplot, the x-axis is this attack rate transformed using the inverse hyperbolic sine function. In both graphs, a vertical line of dots is visible when x = 0, and the OLS line of best fit slopes downwards. Two red boxes highlight the vertical lines of dots where x  = 0 in each graph.
Displays two scatterplots with OLS lines of best fit. In each graph, the y-axis shows values of DVsin. In the left scatterplot, the x-axis is the raw attack rate (attacks/population in hundreds of thousands), whereas in the right scatterplot, the x-axis is this attack rate transformed using the inverse hyperbolic sine function. In both graphs, a vertical line of dots is visible when x = 0, and the OLS line of best fit slopes downwards.
Time series of different measures of terrorist attacks over time in each of 28 EU member states. The top panel shows national time series for the raw count of terrorist attacks. The bottom panel shows national time series for 'DVsin'. Based on data from WEA.
Email inviting me, a PhD candidate, to be the editor-in-chief of a brand new journal of my choosing.
Paper title: The Need for Equivalence Testing in Economics. Written by Jack Fitzgerald, Vrije Universiteit Amsterdam and Tinbergen Institute. Date: February 5, 2025. Abstract: Equivalence testing can provide statistically significant evidence that economic relationships are practically negligible. I demonstrate its necessity in a large-scale reanalysis of estimates defending 135 null claims made in 81 recent articles from top economics journals. 36-63% of estimates defending the average null claim fail lenient equivalence tests. In a prediction platform survey, researchers accurately predict that equivalence testing failure rates will significantly exceed levels which they deem acceptable. Obtaining equivalence testing failure rates that these researchers deem acceptable requires arguing that nearly 75% of published estimates in economics are practically equal to zero. These results imply that Type II error rates are unacceptably high throughout economics, and that many null findings in economics reflect low power rather than truly negligible relationships. I provide economists with guidelines and commands in Stata and R for conducting credible equivalence testing and practical significance testing in future research.
Abstract of the paper; can be accessed at the URL in the post.
An estimate (18000), standard error (3000), smallest effect size of interest (10000), and significance level (0.05) have been supplied to the left-hand panel. A notice at the top indicates that results are asymptotically approximate. Blue text indicates the 90% confidence interval. Red text indicates the 95% confidence interval. Red text denotes that the superiority test is the relevant test (because the estimate is above the upper delta bound). Black text provides the test z-statistic, p-value, and practical significance conclusion. A graph below plots the estimate in black, a 90% confidence interval in blue, and a 95% confidence interval in red, all of which are above the upper delta bound of 10000.
In the inferiority region, a 95% confidence interval is used to assess whether the estimate is significantly bounded beneath the lower delta bound. In the inferiority region, a 90% confidence interval is used to assess whether the estimate is significantly bounded within the delta bounds, even if the 95% confidence interval crosses one of the delta bounds. In the superiority region, a 95% confidence interval is used to assess whether the estimate is significantly bounded above the upper delta bound. The bottom four estimates have confidence intervals crossing the delta bounds, implying that results are inconclusive.
Panel A shows an inferiority test, where H0 states that the estimate is greater than the lower delta bound and HA states that the estimate is less the lower delta bound. Panel B shows a TOST procedure, where the null hypothesis is that the estimate is either above the upper delta bound or below the lower delta bound, and the alternative hypothesis is that the estimate is between the delta bounds. Panel C shows a superiority test, where the null hypothesis states that the estimate is less than the upper delta bound, and the alternative hypothesis states that the estimate is above the upper delta bound. Panel D shows a three-sided tests, where the inferiority region is bounded below the lower delta bound, the equivalence region is bounded between the delta bounds, and the superiority region is bounded above the upper delta bound.
Pink estimate is statistically significantly bounded between the delta bounds. Blue estimate is statistically significantly bounded above the upper delta bound. The confidence interval of the orange estimate intersects one of the delta bound, but does not cross zero.
Pink estimate is statistically significantly bounded between the delta bounds. Blue estimate is statistically significantly bounded above the upper delta bound. The confidence interval of the orange estimate intersects one of the delta bound, but does not cross zero.
In the inferiority region, a 95% confidence interval is used to assess whether the estimate is significantly bounded beneath the lower delta bound. In the inferiority region, a 90% confidence interval is used to assess whether the estimate is significantly bounded within the delta bounds, even if the 95% confidence interval crosses one of the delta bounds. In the superiority region, a 95% confidence interval is used to assess whether the estimate is significantly bounded above the upper delta bound. The bottom four estimates have confidence intervals crossing the delta bounds, implying that results are inconclusive.
Presenting talk.
Presenting talk.