Inhoudsopgave
Content ............................................................................................................................................................ 2
Terminology ........................................................................................................................................................ 2
Multivariate regression analysis ....................................................................................................................... 4
Ordinary Least Squares (OLS) estimation .......................................................................................................... 6
Inference ............................................................................................................................................................. 6
Assumptions ....................................................................................................................................................... 7
Hypothesis testing .......................................................................................................................................... 14
P-value and confidence interval ..................................................................................................................... 19
Joint hypotheses ............................................................................................................................................ 22
OVB ................................................................................................................................................................ 29
Functional form .............................................................................................................................................. 36
Variables in log vs levels ................................................................................................................................... 36
Dummy variable ............................................................................................................................................... 41
Interaction effects .......................................................................................................................................... 53
Multicollinearity ............................................................................................................................................. 57
Imperfect multicollinearity ............................................................................................................................... 59
Heteroskedasticity.......................................................................................................................................... 65
1
,Lecture 1
This lecture is recorderd!
Book is only recommended for those who find the slides difficult. E-book: downloaded on 13
nov 2023 on laptop
On the exam: no STATA codes writing, but we have to read and work with the output
Content
- Regression analysis
o Bivariate regression analysis (2 variables, 1 variable is used to explain another)
o Multivariate regression analysis (multiple variables are used to explain 1 other
variable)
o Ordinary least squares (OLS) estimation: properties and assumptions
Terminology
Inference: using the random sample n to say something about the population N
2
,Beta0: constant/intercept & beta1: slope of the line
Ordinary Least Squares
An illustration of OLS
Residuals: all points that are not on the line, and their distances from the lines
We take the square because the otherwise their would be a difference in positive and
negative residuals
3
, Multivariate regression analysis
We can still use OLS to calculate the unknown
Why use multiple regression?
- Interested in partial: holding variable 1 constant (ceteris parabus), what is the effect
of the other variable on Y?
4
Content ............................................................................................................................................................ 2
Terminology ........................................................................................................................................................ 2
Multivariate regression analysis ....................................................................................................................... 4
Ordinary Least Squares (OLS) estimation .......................................................................................................... 6
Inference ............................................................................................................................................................. 6
Assumptions ....................................................................................................................................................... 7
Hypothesis testing .......................................................................................................................................... 14
P-value and confidence interval ..................................................................................................................... 19
Joint hypotheses ............................................................................................................................................ 22
OVB ................................................................................................................................................................ 29
Functional form .............................................................................................................................................. 36
Variables in log vs levels ................................................................................................................................... 36
Dummy variable ............................................................................................................................................... 41
Interaction effects .......................................................................................................................................... 53
Multicollinearity ............................................................................................................................................. 57
Imperfect multicollinearity ............................................................................................................................... 59
Heteroskedasticity.......................................................................................................................................... 65
1
,Lecture 1
This lecture is recorderd!
Book is only recommended for those who find the slides difficult. E-book: downloaded on 13
nov 2023 on laptop
On the exam: no STATA codes writing, but we have to read and work with the output
Content
- Regression analysis
o Bivariate regression analysis (2 variables, 1 variable is used to explain another)
o Multivariate regression analysis (multiple variables are used to explain 1 other
variable)
o Ordinary least squares (OLS) estimation: properties and assumptions
Terminology
Inference: using the random sample n to say something about the population N
2
,Beta0: constant/intercept & beta1: slope of the line
Ordinary Least Squares
An illustration of OLS
Residuals: all points that are not on the line, and their distances from the lines
We take the square because the otherwise their would be a difference in positive and
negative residuals
3
, Multivariate regression analysis
We can still use OLS to calculate the unknown
Why use multiple regression?
- Interested in partial: holding variable 1 constant (ceteris parabus), what is the effect
of the other variable on Y?
4
