Introduction to Econometrics presentation2

1. Donald Trump’s sentiment
on twitter and the retweet
counts

2. My Assumption
If his tweet is emotionally intensive, either positive or
negative, he will get more Retweets.

3. Data
7374 tweets from July 16, 2015 to November 11, 2016
https://www.kaggle.com/kingburrito666/better-donald-trump-tweets

4. Data
I scored by using a Python package “textbolb”, and named
the column as “Polarity”.
Polarity scaled -10 to 10.
“MAKE AMERICA GREAT AGAIN!” = 10
“Wow, Twitter, Google and Facebook are burying the FBI
criminal investigation of Clinton. Very dishonest media!”
= -2.625

5. Hypothesis and model
“More Emotional, either positive or negative, more Retweets”
Retweets= 𝜷 𝟎+𝜷 𝟏Polarity+𝜷 𝟐 𝑷𝒐𝒍𝒂𝒓𝒊𝒕𝒚 𝟐+u
should be a quadratic line with concaving up
𝛽2>0 , 𝛽0 −
𝛽2
2
4𝛽1
2 > 0
Explanatory variables are statically significant
Satisfies MRS assumptions

6. OLS model
Successfully Achieved data and conduct regression by R
𝑹𝒆𝒕𝒘𝒆𝒆𝒕𝒔= 𝟒𝟑𝟔𝟔. 𝟗𝟔𝟏-151.191Polarity+ 𝟏𝟎. 𝟔𝟐𝟕𝑷𝒐𝒍𝒂𝒓𝒊𝒕𝒚 𝟐
Global min :
(Polarity, Retweets) =
(7.114, 3829.21)
VIF= 1.524855

7. Breusch-Pagan Test
Is this model Homoscedastic??
𝑢2 = δ0 + δ1 𝑃𝑜𝑙𝑎𝑟𝑖𝑡𝑦 + δ2 𝑃𝑜𝑙𝑎𝑟𝑖𝑡𝑦2 + 𝑒𝑟𝑟𝑜𝑟
𝐻0 ∶ δ1=δ2 = 0
𝐻1 ∶ 𝐻0 𝑖𝑠 𝑛𝑜𝑡 𝑐𝑜𝑟𝑟𝑒𝑐𝑡
5.093 at degree of freedom 7372,
the p-value is 0.6158%
heteroscedastic

8. Robust standard error
Robust Standard Error Polarity Polarity^2
25.914264 6.426094
T value Polarity Polarity^2
-5.834277 1.653726
Statistically insignificant

9. FGLS model
𝑅𝑒𝑡𝑤𝑒𝑒𝑡𝑠= 4355.74 - 204.09Polarity+ 17.50𝑃𝑜𝑙𝑎𝑟𝑖𝑡𝑦2
(Polarity, Retweets) = (5.831, 3760.70)
Looks Fine!