https://github.com/greatarcstudios/non-linear-election-predictions
Predicting Elections Using GAMs and Post-Stratification
https://github.com/greatarcstudios/non-linear-election-predictions
election-prediction generalized-additive-models multilevel-models post-stratification prediction
Last synced: about 1 month ago
JSON representation
Predicting Elections Using GAMs and Post-Stratification
- Host: GitHub
- URL: https://github.com/greatarcstudios/non-linear-election-predictions
- Owner: GreatArcStudios
- Created: 2022-12-02T04:26:17.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-02T05:17:30.000Z (over 2 years ago)
- Last Synced: 2025-02-13T22:37:55.667Z (3 months ago)
- Topics: election-prediction, generalized-additive-models, multilevel-models, post-stratification, prediction
- Homepage:
- Size: 36.5 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Non-Linear Election Predictions
A lot of literature involving election predictions uses MRP, which often uses Bayesian methods, i.e., Bayesian logistic mixed effects regression. What if we allowed for non-linearities in our predictor through GAMs?
It is suprisingly effective! This project was done on Canadian election data, using the CES 2019 online survey and GSS data as census data for post-stratification.
This project was originally started as an assignment for STA304/1003 (assignment 2).
Please read our [manuscript](https://github.com/GreatArcStudios/Non-Linear-Election-Predictions/blob/main/Submission/Assignment2.pdf) for more information as this is being written (to be finished at some point).