Time series analysis using Stata: Twitter behaviour Sorry, I'm quite new at Time series analysis.
I'm trying to conduct a study on the Twitter behaviour of Donald Trump and how it affects his popularity rating. I have collected the data on his tweets, created dummy's for the themes of the tweets (e.g. Obama, China, tax reforms, immigrants, etc.), time of day, etc. My questions:
Question 1. Does anyone know of a similar study done in Stata I can consult?
Question 2. Using Stata, I have to do a regression analysis of this time series. However, I have some problems getting started. First of all with the selection of the model and which steps to do first. Would this be a valid order:
A. Test for stationarity (unit root Test Dickey-Fuller)
B. Test for cointegration, what test to use in Stata? (Johansen or Engle-Granger?)
C. Choose model (any general step-by-step guide to choose one?)
Question 3. The non uniformity of my data: the number of tweets vary each day. I thought of considering the different tweets like panel data. However, as some days have 7 tweets and some have only 1, it would look like I have a lot of observations missing. Can I give each observation (tweet) a code so Stata knows it is still from the same day?
If these questions seem irrelevant or not well thought-through, it is because I'm really struggling to grasp what is important in order to conduct my study. I am lost in the abundance of theory on time series and Stata so any sense of direction would be much appreciated!
 A: Question 2. Using Stata, I have to do a regression analysis of this time series
In my opinion I don't believe Stata will be of use to you as it lacks broad functionality in time series analysis. Please review http://autobox.com/dave/regvsbox.pdf (which I authored) discusses issues/differences/opportunities/pitfalls when dealing with time series that your possible regression solutions may be ignoring.
You might also look at If I am convinced that a series is mostly trend+season, what is it I should check about the residuals? as it discusses the opportunities/complications that arise with time series data. It specifically deals with residuals from a specified model but the specified model could easily be be a simple (standard) regression model.
Unknown/untreated factors can often play havoc with model identification such as pulses/level shifts/local time trends , changes in error variance , changes in parameters over time which is why they need to be empirically identified and controlled/adjusted for.

The above flow chart might also be of help in providing a framework/script to follow regardless of your software of choice.
