Ch2: Woolridge: The Simple Regression Model Flashcards
What is the simple regression model used for?
To study the relationship between two variables
What is u?
The error term or disturbance in the relationship. It represents factors other than x that affects y. the model treats all of those factors as being unobserved. so think of u as unobserved
What is b0?
the intercept parameter, aka constant term
What is the natural measure of the association between two random variables?
the correlation coefficient
What happens if u and x are uncorrelated?
there will not be linearly related
What is the residual?
The difference between the actual value and its fitted value. (actual - estimated) y - yhat
What is the constant elasticity model?
The constant elasticity model is a way to describe how one thing (like price) affects another thing (like quantity) in a consistent way, no matter how big or small the values are.
In a constant elasticity model:
- Elasticity tells us how much one thing changes in percentage when another thing changes by 1%.
- Constant means the percentage change stays the same no matter what.
In simple terms, the constant elasticity model says, “If I change one thing by a certain percentage, another thing changes by a fixed percentage every time.”
What is the zero conditional mean?
The zero conditional mean is a rule in econometrics that says the average of the error terms (the “mistakes” in the model, u, the residual) should be zero when you have certain conditions.
The zero conditional mean means that, on average, your guesses will be correct (you don’t systematically overestimate or underestimate).
In more technical terms, it means that the average value of the errors should be zero when the values of the independent variables are known. This is important because it ensures that your model doesn’t have bias and that your guesses are fair.
What is homoskedasticity?
this assumption says that the variance of the unobservable, u, conditional on x, is constant. aka constant error variance assumption.
it means that the mistakes you make in your predictions are spread out evenly for all values of your predictor.
What is heteroskadasicity?
if your mistakes are bigger for large jars and smaller for small jars, that’s heteroskedasticity, which means the spread of the mistakes changes with the size of the jar.
What is the difference between errors and residuals?
Errors are unknown and represent the true mistakes.
Residuals are what we actually calculate based on our model’s predictions, acting as estimates of the errors.
What does squared error explain?
How much of the total variation is not described by the regression line?
What is the coefficient of determination? (also called r squared) (khan)
tells what % of total variation is described by the line (the variation in x). tells us how good of a fit the line is in the plot relative to observer points!
What is the exogeneity of covariates?
The exogeneity of covariates means that the independent variables (or predictors) in a regression model are not related to the error term. It’s a key assumption in econometrics that ensures your model gives reliable and unbiased estimates of the coefficients.
