Week 5: Inference and Introduction to Gauss-Markov Theorem (02/09/2023)

Ozlem Tuncel

otuncelgurlek1@gsu.edu

⚠️ CAUTION: DO NOT SOLELY RELY ON MY NOTES. THERE MIGHT BE TYPOS AND MISTAKES. ALWAYS TAKE YOUR NOTES!

✔️ The goal of this week is to learn matrix notation for OLS regression and get familiar with Gauss-Markov theorem.

Here are some key points:

• This is a math heavy week so prepare yourself. For this week, we are mostly interested in Gauss-Markov theorem, and we will go into details of assumptions next week. For this week, we have to blindly make some assumptions.
• Our estimators $\hat{\beta_0}$ and $\hat{\beta_1}$ are random variables.
• Conflict data example: you have the whole population in Correlates of War (you know every conflict out there) and why you are using inferential statistics given that you have the question? Quick answer: parallel universe argument. Confused? see here and here.
• Goal of the applied social science: eplain some outcome with some set of variables. Can we really do that? NO. Humans are inpredictable. That’s why things that we cannot explain systematically goes into the error term.
• Alone, point estimates are useless. That’s why we are mostly interested in the variance. We hope $E(\bar{x}) = \mu$. Thus, we are mostly interested in the precision of our estimates - this helps us to make meaningful inferences.
• Gauss-Markov Theorem is the most important thing for this class - we understand when we can use OLS regression and cannot use it.
• Key assumptions we make:
  • X is fixed (we will explain this later)
  • Y has both systematic and random variation (randomness goes into the error term, and we assume error terms is i.i.d and normally distributed).
  • i.i.d. = independently and identically distributed
  • $u_i \sim i.i.d. N(0, \sigma^2)$

  • $Var(Y

    X\beta) = \sigma^2$ - stochastic (random) variation in Y

• Remember that $\sigma^2$ is population standard deviation
• Simply having more variation in our independent variable is going to lead to larger variation in our estimators.
• Z score is the estimate divided by the standard error => higher z score, low p-value
• If you have enough number of observations, you will get statistical significance. So, small number of sample size is bad, high number of sample size is meaningless.
• Best means minimum variance among other estimators

Some suggestions:

• Here is an exploratory regression shiny application
• More sources on $y$, $\hat{y}$, and residuals: source 1, source 2, source 3, source 4
• Central Limit Theorem simulator