内容简介:Last update on Thursday June 11, 2020 at 07:37 AM EDTCode for a dynamic multilevel Bayesian model to predict US presidential elections. Written in R and Stan.Improving on Pierre Kremp’s
State and national presidential election forecasting model
Last update on Thursday June 11, 2020 at 07:37 AM EDT
Code for a dynamic multilevel Bayesian model to predict US presidential elections. Written in R and Stan.
Improving on Pierre Kremp’s implementation of Drew Linzer’s dynamic linear model for election forecasting (Linzer 2013) , we (1) add corrections for partisan non-response, survey mode and survey population; (2) use informative state-level priors that update throughout the election year; and (3) specify empirical state-level correlations from political and demographic variables.
You can see the model’s predictions for 2020 here and read how it works here .
File dictionary
In terms of useful files, you should pay attention to the 3 scripts for the 2008, 2012 and 2016 US presidential elections are located in the scripts/model
directory. There are three R scripts that import data, run models and parse results:
final_model_2008.R final_model_2012.R final_model_2016.R
And there are 3 different Stan scripts that will run different versions of our polling aggregate and election forecasting model:
poll_model_2020.stan poll_model_2020_no_partisan_correction.stan poll_model_2020_no_mode_adjustment.stan
The model diagnostics displayed below are all results of the poll_model_2020.stan
script.
Model performance
Here is a graphical summary of the model’s performance in 2008, 2012 and 2016.
2008
Map
Final electoral-college histogram
National and state polling averages and the electoral college “now-cast” over time
States’ partisan leans over time
Model results vs polls vs the prior
Performance
outlet | ev_wtd_brier | unwtd_brier | states_correct |
---|---|---|---|
economist (backtest) | 0.0333707 | 0.0302863 | 49 |
## [1] 0.02242826
Predictions for each state
state | mean | low | high | prob | se |
---|---|---|---|---|---|
NC | 0.503 | 0.454 | 0.555 | 0.527 | 0.031 |
MO | 0.510 | 0.461 | 0.561 | 0.635 | 0.030 |
IN | 0.486 | 0.434 | 0.537 | 0.330 | 0.031 |
MT | 0.483 | 0.427 | 0.534 | 0.297 | 0.031 |
FL | 0.519 | 0.472 | 0.569 | 0.734 | 0.030 |
GA | 0.476 | 0.425 | 0.528 | 0.201 | 0.031 |
VA | 0.527 | 0.479 | 0.574 | 0.818 | 0.028 |
OH | 0.527 | 0.478 | 0.576 | 0.818 | 0.029 |
AR | 0.472 | 0.423 | 0.524 | 0.190 | 0.031 |
AZ | 0.470 | 0.418 | 0.523 | 0.172 | 0.032 |
WV | 0.469 | 0.420 | 0.520 | 0.154 | 0.031 |
NV | 0.534 | 0.483 | 0.587 | 0.859 | 0.031 |
MS | 0.465 | 0.412 | 0.522 | 0.134 | 0.034 |
CO | 0.535 | 0.485 | 0.583 | 0.878 | 0.028 |
ND | 0.463 | 0.403 | 0.520 | 0.150 | 0.034 |
LA | 0.461 | 0.412 | 0.514 | 0.102 | 0.031 |
TX | 0.458 | 0.407 | 0.508 | 0.092 | 0.030 |
– | 0.544 | 0.513 | 0.575 | 0.996 | 0.018 |
SC | 0.455 | 0.408 | 0.508 | 0.066 | 0.032 |
SD | 0.453 | 0.401 | 0.506 | 0.074 | 0.031 |
NH | 0.552 | 0.503 | 0.602 | 0.956 | 0.030 |
PA | 0.557 | 0.508 | 0.606 | 0.966 | 0.029 |
WI | 0.559 | 0.507 | 0.606 | 0.974 | 0.028 |
KY | 0.440 | 0.391 | 0.491 | 0.030 | 0.031 |
NM | 0.560 | 0.502 | 0.614 | 0.951 | 0.032 |
MN | 0.561 | 0.512 | 0.611 | 0.976 | 0.030 |
TN | 0.438 | 0.388 | 0.491 | 0.030 | 0.032 |
IA | 0.564 | 0.512 | 0.612 | 0.978 | 0.029 |
MI | 0.565 | 0.516 | 0.612 | 0.981 | 0.028 |
AK | 0.427 | 0.375 | 0.479 | 0.007 | 0.031 |
OR | 0.575 | 0.524 | 0.624 | 0.993 | 0.030 |
KS | 0.424 | 0.375 | 0.475 | 0.009 | 0.030 |
ME | 0.587 | 0.537 | 0.635 | 0.997 | 0.029 |
WA | 0.587 | 0.536 | 0.636 | 0.998 | 0.030 |
NE | 0.412 | 0.363 | 0.462 | 0.001 | 0.029 |
AL | 0.407 | 0.359 | 0.460 | 0.009 | 0.031 |
NJ | 0.594 | 0.543 | 0.644 | 0.999 | 0.030 |
DE | 0.619 | 0.569 | 0.666 | 0.999 | 0.028 |
CA | 0.621 | 0.569 | 0.669 | 1.000 | 0.028 |
OK | 0.379 | 0.330 | 0.432 | 0.001 | 0.031 |
CT | 0.623 | 0.576 | 0.670 | 1.000 | 0.028 |
WY | 0.375 | 0.325 | 0.426 | 0.000 | 0.031 |
MD | 0.629 | 0.568 | 0.687 | 0.999 | 0.034 |
IL | 0.633 | 0.586 | 0.679 | 1.000 | 0.027 |
MA | 0.636 | 0.586 | 0.686 | 1.000 | 0.030 |
ID | 0.352 | 0.301 | 0.404 | 0.000 | 0.031 |
NY | 0.649 | 0.599 | 0.699 | 1.000 | 0.029 |
UT | 0.344 | 0.297 | 0.393 | 0.000 | 0.029 |
VT | 0.660 | 0.611 | 0.707 | 1.000 | 0.028 |
RI | 0.669 | 0.622 | 0.716 | 1.000 | 0.028 |
HI | 0.677 | 0.618 | 0.727 | 1.000 | 0.030 |
DC | 0.908 | 0.880 | 0.934 | 1.000 | 0.015 |
2012
Map
Final electoral-college histogram
National and state polling averages and the electoral college “now-cast” over time
States’ partisan leans over time
Model results vs polls vs the prior
Performance
outlet | ev_wtd_brier | unwtd_brier | states_correct |
---|---|---|---|
Linzer | NA | 0.003800 | NA |
Wang/Ferguson | NA | 0.007610 | NA |
Silver/538 | NA | 0.009110 | NA |
Jackman/Pollster | NA | 0.009710 | NA |
Desart/Holbrook | NA | 0.016050 | NA |
economist (backtest) | 0.0327484 | 0.021624 | 50 |
Intrade | NA | 0.028120 | NA |
Enten/Margin of Error | NA | 0.050750 | NA |
## [1] 0.02187645
Predictions for each state
state | mean | low | high | prob | se |
---|---|---|---|---|---|
FL | 0.497 | 0.450 | 0.542 | 0.468 | 0.027 |
VA | 0.506 | 0.461 | 0.550 | 0.591 | 0.026 |
CO | 0.510 | 0.465 | 0.557 | 0.645 | 0.028 |
OH | 0.511 | 0.465 | 0.558 | 0.656 | 0.028 |
– | 0.512 | 0.480 | 0.542 | 0.773 | 0.018 |
NC | 0.488 | 0.442 | 0.534 | 0.343 | 0.027 |
NH | 0.517 | 0.470 | 0.564 | 0.726 | 0.028 |
IA | 0.518 | 0.473 | 0.561 | 0.749 | 0.026 |
NV | 0.521 | 0.474 | 0.568 | 0.787 | 0.028 |
WI | 0.524 | 0.478 | 0.569 | 0.805 | 0.027 |
PA | 0.531 | 0.485 | 0.577 | 0.862 | 0.027 |
MO | 0.463 | 0.416 | 0.510 | 0.095 | 0.028 |
MN | 0.538 | 0.493 | 0.582 | 0.916 | 0.027 |
OR | 0.539 | 0.492 | 0.585 | 0.919 | 0.027 |
AZ | 0.459 | 0.412 | 0.504 | 0.072 | 0.027 |
MI | 0.541 | 0.495 | 0.586 | 0.928 | 0.027 |
IN | 0.458 | 0.413 | 0.504 | 0.062 | 0.027 |
NM | 0.543 | 0.496 | 0.589 | 0.936 | 0.027 |
MT | 0.455 | 0.409 | 0.502 | 0.056 | 0.028 |
GA | 0.453 | 0.405 | 0.499 | 0.049 | 0.028 |
SC | 0.441 | 0.386 | 0.496 | 0.043 | 0.033 |
NJ | 0.559 | 0.510 | 0.606 | 0.975 | 0.028 |
SD | 0.439 | 0.392 | 0.487 | 0.016 | 0.029 |
ME | 0.561 | 0.515 | 0.606 | 0.982 | 0.027 |
WA | 0.565 | 0.519 | 0.612 | 0.987 | 0.028 |
CT | 0.571 | 0.523 | 0.619 | 0.993 | 0.029 |
ND | 0.425 | 0.380 | 0.469 | 0.005 | 0.026 |
TN | 0.425 | 0.378 | 0.472 | 0.004 | 0.028 |
NE | 0.422 | 0.375 | 0.470 | 0.003 | 0.028 |
WV | 0.418 | 0.368 | 0.469 | 0.002 | 0.031 |
MS | 0.415 | 0.351 | 0.478 | 0.015 | 0.037 |
TX | 0.415 | 0.373 | 0.461 | 0.002 | 0.027 |
CA | 0.590 | 0.547 | 0.632 | 0.998 | 0.025 |
MA | 0.590 | 0.541 | 0.637 | 0.996 | 0.028 |
LA | 0.405 | 0.359 | 0.452 | 0.002 | 0.028 |
KY | 0.405 | 0.354 | 0.455 | 0.000 | 0.030 |
KS | 0.399 | 0.342 | 0.460 | 0.005 | 0.036 |
DE | 0.604 | 0.549 | 0.659 | 0.997 | 0.033 |
IL | 0.604 | 0.556 | 0.649 | 1.000 | 0.027 |
MD | 0.607 | 0.559 | 0.653 | 1.000 | 0.027 |
AL | 0.389 | 0.342 | 0.437 | 0.000 | 0.028 |
AR | 0.378 | 0.333 | 0.425 | 0.000 | 0.028 |
RI | 0.623 | 0.573 | 0.672 | 1.000 | 0.029 |
NY | 0.623 | 0.576 | 0.668 | 1.000 | 0.027 |
AK | 0.366 | 0.306 | 0.428 | 0.001 | 0.037 |
OK | 0.339 | 0.290 | 0.393 | 0.000 | 0.032 |
HI | 0.662 | 0.615 | 0.707 | 1.000 | 0.027 |
VT | 0.674 | 0.622 | 0.721 | 1.000 | 0.028 |
ID | 0.325 | 0.276 | 0.375 | 0.000 | 0.030 |
WY | 0.317 | 0.250 | 0.391 | 0.000 | 0.044 |
UT | 0.275 | 0.231 | 0.323 | 0.000 | 0.029 |
DC | 0.899 | 0.848 | 0.938 | 1.000 | 0.023 |
2016
Map
Final electoral-college histogram
National and state polling averages and the electoral college “now-cast” over time
States’ partisan leans over time
Model results vs polls vs the prior
Performance
outlet | ev_wtd_brier | unwtd_brier | states_correct |
---|---|---|---|
economist (backtest) | 0.0864787 | 0.0602528 | 47 |
538 polls-plus | 0.0928000 | 0.0664000 | 46 |
538 polls-only | 0.0936000 | 0.0672000 | 46 |
princeton | 0.1169000 | 0.0744000 | 47 |
nyt upshot | 0.1208000 | 0.0801000 | 46 |
kremp/slate | 0.1210000 | 0.0766000 | 46 |
pollsavvy | 0.1219000 | 0.0794000 | 46 |
predictwise markets | 0.1272000 | 0.0767000 | 46 |
predictwise overall | 0.1276000 | 0.0783000 | 46 |
desart and holbrook | 0.1279000 | 0.0825000 | 44 |
daily kos | 0.1439000 | 0.0864000 | 46 |
huffpost | 0.1505000 | 0.0892000 | 46 |
## [1] 0.03073332
Predictions for each state
state | mean | low | high | prob | se |
---|---|---|---|---|---|
FL | 0.502 | 0.457 | 0.547 | 0.509 | 0.027 |
NC | 0.496 | 0.448 | 0.542 | 0.455 | 0.027 |
OH | 0.492 | 0.447 | 0.537 | 0.386 | 0.027 |
IA | 0.491 | 0.443 | 0.541 | 0.384 | 0.030 |
NV | 0.514 | 0.465 | 0.562 | 0.676 | 0.029 |
– | 0.517 | 0.487 | 0.547 | 0.846 | 0.018 |
PA | 0.519 | 0.474 | 0.566 | 0.747 | 0.028 |
CO | 0.520 | 0.475 | 0.566 | 0.763 | 0.028 |
NH | 0.521 | 0.475 | 0.567 | 0.764 | 0.028 |
AZ | 0.475 | 0.425 | 0.525 | 0.204 | 0.029 |
MI | 0.527 | 0.482 | 0.572 | 0.844 | 0.027 |
WI | 0.528 | 0.485 | 0.574 | 0.846 | 0.027 |
VA | 0.528 | 0.483 | 0.572 | 0.857 | 0.026 |
GA | 0.471 | 0.426 | 0.518 | 0.148 | 0.028 |
MN | 0.534 | 0.486 | 0.580 | 0.884 | 0.027 |
NM | 0.540 | 0.495 | 0.587 | 0.918 | 0.028 |
SC | 0.456 | 0.410 | 0.501 | 0.052 | 0.027 |
ME | 0.551 | 0.506 | 0.597 | 0.968 | 0.027 |
MO | 0.446 | 0.401 | 0.492 | 0.025 | 0.028 |
OR | 0.555 | 0.509 | 0.600 | 0.976 | 0.027 |
TX | 0.442 | 0.397 | 0.488 | 0.018 | 0.027 |
MS | 0.441 | 0.394 | 0.488 | 0.021 | 0.028 |
WA | 0.573 | 0.528 | 0.619 | 0.996 | 0.027 |
CT | 0.573 | 0.526 | 0.616 | 0.995 | 0.026 |
AK | 0.426 | 0.380 | 0.470 | 0.003 | 0.026 |
IN | 0.426 | 0.379 | 0.472 | 0.002 | 0.028 |
DE | 0.576 | 0.532 | 0.621 | 0.997 | 0.027 |
NJ | 0.580 | 0.532 | 0.626 | 0.997 | 0.028 |
MT | 0.413 | 0.368 | 0.460 | 0.001 | 0.028 |
LA | 0.412 | 0.368 | 0.456 | 0.002 | 0.027 |
KS | 0.412 | 0.362 | 0.460 | 0.001 | 0.029 |
IL | 0.589 | 0.542 | 0.635 | 0.999 | 0.028 |
TN | 0.407 | 0.363 | 0.453 | 0.000 | 0.027 |
RI | 0.594 | 0.545 | 0.643 | 0.999 | 0.029 |
SD | 0.405 | 0.359 | 0.452 | 0.000 | 0.028 |
UT | 0.395 | 0.325 | 0.452 | 0.000 | 0.034 |
NE | 0.393 | 0.347 | 0.440 | 0.000 | 0.028 |
NY | 0.610 | 0.566 | 0.655 | 1.000 | 0.027 |
AR | 0.390 | 0.345 | 0.437 | 0.000 | 0.028 |
AL | 0.389 | 0.344 | 0.434 | 0.000 | 0.027 |
ND | 0.387 | 0.339 | 0.435 | 0.000 | 0.029 |
KY | 0.378 | 0.334 | 0.422 | 0.000 | 0.026 |
CA | 0.624 | 0.581 | 0.666 | 1.000 | 0.025 |
MA | 0.631 | 0.585 | 0.676 | 1.000 | 0.027 |
MD | 0.638 | 0.592 | 0.682 | 1.000 | 0.026 |
ID | 0.359 | 0.317 | 0.407 | 0.000 | 0.028 |
WV | 0.358 | 0.316 | 0.402 | 0.000 | 0.026 |
HI | 0.653 | 0.605 | 0.700 | 1.000 | 0.028 |
OK | 0.346 | 0.302 | 0.391 | 0.000 | 0.027 |
VT | 0.676 | 0.629 | 0.719 | 1.000 | 0.026 |
WY | 0.290 | 0.251 | 0.335 | 0.000 | 0.026 |
DC | 0.906 | 0.870 | 0.936 | 1.000 | 0.018 |
Cumulative charts
Calibration plot
Licence
This software is published by The Economist under the MIT licence . The data generated by The Economist are available under the Creative Commons Attribution 4.0 International License .
The licences include only the data and the software authored by The Economist , and do not cover any Economist content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of Economist content can be found here .
以上所述就是小编给大家介绍的《A dynamic multilevel Bayesian model to predict US presidential elections》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
猜你喜欢:本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
Flow-Based Programming (2nd Edition)
CreateSpace / 2010-5-14 / $69.95
Written by a pioneer in the field, this is a thorough guide to the cost- and time-saving advantages of Flow-Based Programming. It explains the theoretical underpinnings and application of this program......一起来看看 《Flow-Based Programming (2nd Edition)》 这本书的介绍吧!