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NBER isn't good at models
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# data digitized from https://twitter.com/nberpubs/status/1062080096549261312 | |
# thanks @tpoi and co! https://cran.r-project.org/web/packages/digitize/index.html | |
# load the above data | |
dat <- read.csv("nber.csv") | |
# it's ya boi, mgcv | |
library(mgcv) | |
# fit the next stupidest model | |
b <- gam(y~s(x), data=dat) | |
# make predictions for plotting, incl uncertainty | |
preddat <- data.frame(x=seq(min(dat$x), max(dat$x), len=200)) | |
pred <- predict(b, preddat, se=TRUE, unconditional=TRUE) | |
preddat$y <- pred$fit | |
# make +/- 2 std errors | |
preddat$upper <- pred$fit + 2*pred$se | |
preddat$lower <- pred$fit - 2*pred$se | |
# make a plotto | |
plot(dat, xlab="90th percentile-earner personal-income marginal tax rate(%)", ylab="log patents", type="n") | |
polygon(c(preddat$upper, rev(preddat$lower)), | |
x=c(preddat$x, rev(preddat$x)), | |
border=FALSE, col=grey(.8)) | |
lines(preddat$x, preddat$y) | |
points(dat, pch=19, cex=0.6) |
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x | y | |
---|---|---|
-2.75506828 | -0.077672676 | |
-1.99675696 | -0.110666901 | |
-2.26950144 | 0.023157974 | |
-1.84655654 | -0.005664207 | |
-1.65585928 | -0.030414573 | |
-1.42045090 | 0.001646266 | |
-1.52673038 | 0.037121165 | |
-1.05459664 | 0.051817282 | |
-1.27775720 | 0.081002794 | |
-0.91249558 | 0.015058196 | |
-0.75182524 | 0.026139795 | |
-1.17174112 | 0.101311751 | |
-0.97129829 | 0.112781738 | |
-0.83769168 | 0.084824035 | |
-0.69657835 | 0.126795044 | |
-0.63349549 | 0.127947680 | |
-0.55796726 | 0.102364158 | |
-0.45728490 | 0.111140484 | |
-0.37596201 | 0.096275230 | |
-0.51668025 | 0.074963980 | |
-0.32460018 | 0.054573587 | |
-0.27468701 | 0.146270845 | |
-0.20416327 | 0.125604821 | |
-0.07220288 | 0.128041645 | |
-0.13851232 | 0.084755127 | |
-0.01537562 | 0.059904532 | |
0.06495954 | 0.042946991 | |
0.13831477 | -0.010230903 | |
0.21147245 | -0.006760465 | |
0.45945790 | -0.026850170 | |
0.52096040 | -0.046206948 | |
0.38076895 | -0.069610484 | |
0.26803632 | -0.107960710 | |
0.34060136 | -0.110441385 | |
0.67517759 | 0.067033339 | |
0.57271732 | 0.005429921 | |
0.75715897 | 0.042176479 | |
0.89616515 | 0.033694576 | |
1.15771539 | -0.012655199 | |
0.94555152 | -0.025290352 | |
1.27248932 | -0.066898029 | |
1.03398606 | -0.159390857 | |
1.41656584 | -0.161433028 | |
1.53272259 | -0.173786286 | |
1.73237524 | -0.142257915 | |
2.35029755 | -0.152832103 | |
1.88705336 | -0.264236942 | |
2.07630195 | -0.283625042 | |
2.82598711 | -0.273044589 | |
3.82392111 | -0.111199369 |
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The resulting plot: