Wikipedia won't let me edit it. You could just undo the most recent edit and it should be good.
https://en.wikipedia.org/wiki/United_States_presidential_approval_rating#Historical_comparison
Edit: looks like some patriot took care of it. Thanks!
This is the best tl;dr I could make, original reduced by 48%. (I'm a bot)
> Respect for the United States has skyrocketed around the globe now that former president Donald Trump is out of office.
> While Biden has only been in office for six months, the median approval rating of the US leadership has soared to 49 percent, a number not seen since the beginning of former President Barack Obama's term in office.
> Trump had a 30 percent approval rating at the end of his presidency.
> "But missed opportunities to engage with the rest of the world during all four years - particularly during the pandemic - likely overshadowed any potential goodwill and help explain the 30% approval rating at the end of Trump's term."
> "In the first few hours of his presidency, Biden rejoined the Paris Climate Accord and halted Trump's decision to leave the World Health Organization. And, in his first foreign policy speech as president, Biden vowed to repair U.S. alliances through diplomacy and restore the image of the U.S.," they continued.
> In almost every country, the approval rating soared by at least 10 percent.
Summary Source | FAQ | Feedback | Top keywords: Trump^#1 rating^#2 approval^#3 country^#4 office^#5
Post found in /r/politics, /r/politics and /r/politicly.
NOTICE: This thread is for discussing the submission topic. Please do not discuss the concept of the autotldr bot here.
In this twitter post by an organization called "Data for progress" a univariate linear regression was used to model the relationship between gas prices and presidential approval ratings. The authors used approval ratings at level (Y) and a weekly average of gas prices (x). They found a R^2 / correlation of around 0.96 / 96%, which is extremely high for an empirical regression. This R1 will focus on the econometrics of the claim, rather than the veracity of the claim itself.
What's wrong with the model?
For the vast majority of models in time series econometrics, a requirement for the model to be unbiased / consistent is for the data to be stationary. Put simply, this means that observations are converted into log difference or into a % change instead of using it as is. This is because when we examine things like asset prices, macro or micro economic variables or anything that grows over time, there is a natural upward trend in the movement of these variables. This causes 'false' correlation with the associated data points, biasing inferential statistics and making your model biased.
With this information, we can say that the model used was biased because:
- Contemporaneous correlation: A weekly average of oil prices is not stationary, so a natural upward trend in the price of the asset is in the data, which means that the R^2 of 0.96 he got is wrong and the correlation he establishes is highly biased.
- Volatility clustering in asset prices that see 'jumps' tend to be quite strong. Clustering makes the effects of price jumps and serial correlation more pronounced, making the lack of consideration of auto correlation even worse in his regression.
- Weekly frequency when dealing with gas prices don't reflect the nature of how gas prices behave (they are volatile and are typically examined at higher frequencies)
- It's also a single variable regression, so there are several omitted variables (ie the regression is way too simplistic)
So the model is biased. What does the same model look like with unbiased data?
I first began by replicating the study with the same underlying data as the model used in the twitter post. I used DHHNGSP (fred) for gas prices. For approval ratings, I used all voter approval ratings for Biden from fivethirtyeight. Both are at a daily frequency, beginning
... keep reading on reddit ➡
https://twitter.com/tariqnasheed/status/1461837926757584898
An interesting look at what the 2020 map could look like, based solely on Trump's state-by-state approval ratings (per Morning Consult).
https://www.270towin.com/maps/BPzdE
Key:
- Toss-Up = Trump net approval of 0
- Tilt = Trump net dis/approval of 1-5
- Lean = Trump net dis/approval of 5-10
- Likely = Trump net dis/approval of 10-15
- Solid = Trump net dis/approval of 15+
Now, obviously some of these states aren't likely to be competitive (like Idaho or Oklahoma). But it definitely shows that we have a lot of room to work with, depending on the nominee.
Obama had a high international approval rating, partially due to being "not Bush" when Bush had a low international approval rating.
Do you think that Trump's damaged the institution of the presidency so much that president 46 no matter who they are will have a continuation of Trump's low approval rating?
Get smarts to 100, go to political science as a major. Do 4 years, become lobbyists, by the time your around 55 you should have a decent amount of money. Use about 25 to 45 on becoming mayor. Restart until you win. Check the newspaper and put the main topic as 16% and everything else at 8, do that for next 4 years and run for re-election. If your approval rating is above 85 you don’t need to use that much money for re-election.
Become governor. When you become a famous politician only do social media and talk shows. Use about 25 to 45% of money to run. Repeat what you did as mayor. You should be a famous politician by the end of your 2 terms as governor.
Now that your only option is president, go to grad school and focus on fame, (you can do anything) grad school is 2 years so for the other 2 just do whatever.
Now you run for President, do republican and Democrat because those are your best chances, use about 45 to 65 of your wealth. Congratulations, your now president. You now have more agenda options, put the main issue on 16% and everything else at 5%.
I realize causality can be difficult to prove in social science, but at very least I'd like to know which factors seem predictive in influencing presidential approval ratings.
I'm particularly interested in factors that are measured frequently, but anything measurable will do.
I realize causality can be difficult to prove in social science, but at very least I'd like to know which factors seem predictive in influencing presidential approval ratings.
I'm particularly interested in factors that are measured frequently, but anything measurable will do.
